Blockchain-Based Decentralized Cloud Platform / Abdelhakim Tebbi

Public ISBD Titre : Blockchain-Based Decentralized Cloud Platform Type de document : document électronique Auteurs : Abdelhakim Tebbi ; Goudjil, Lakhdar, Directeur de thèse Editeur : Setif:UFA Année de publication : 2025 Importance : 1 vol (109 f .) Format : 29 cm Langues : Anglais (eng) Catégories : Thèses & Mémoires:Informatique Mots-clés : Blockchain Decentralized Cloud InterPlanetary File System (IPFS) Peer-to-Peer Network Data Sovereignty Distributed Storage Trustless Systems Decen- tralized Infrastructure Cloud Security Index. décimale : 004 Informatique Résumé : Cloud computing has become an essential element of contemporary digital infrastructure, facilitating scalable storage and computational services. However, conventional cloud systems are inherently centralized, which has raised concerns about data control, single points of failure, high costs and the concentration of power among a few large providers. In response to these concerns, this thesis proposes a blockchain-based decentralized cloud infrastructure that creates a peer-to-peer marketplace where individuals can monetize their unused computing and storage resources. The system enables resource providers to join a global network and oer various cloud services through a simple setup process, while users can access these services similar to traditional cloud platforms. The architecture integrates blockchain technology with distributed computing princi- ples to create a trustless environment where service delivery is veried and payments are automated. The blockchain serves as both a verication layer for ensuring services are properly delivered and as a transparent record of all transactions within the network. The system is exemplied through a functional MVP that demonstrates its primary functionalities. Although performance and scalability remain areas for future enhance- ments, this work underscores the viability of decentralized cloud solutions suitable for privacy-sensitive, cost eective and censorship-resistant applications in both personal and organizational settings. Note de contenu : Sommaire Abstract General Introduction 1 1 Cloud Computing 3 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2 Cloud Computing Foundation . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2.1 Denition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2.2 History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2.3 Cloud Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.2.4 Service Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.2.5 Cloud Deployment Models . . . . . . . . . . . . . . . . . . . . . . . 13 1.2.6 Cloud Enabling Technology . . . . . . . . . . . . . . . . . . . . . . 15 1.3 Cloud Computing Challenges . . . . . . . . . . . . . . . . . . . . . . . . . 20 1.3.1 Security Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 1.3.2 Centralized Control . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 1.3.3 Data sovereignty . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2 Blockchain 23 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.2 Distributed Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.2.1 Denition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.2.2 Distributed database system . . . . . . . . . . . . . . . . . . . . . 24 2.2.3 Peer to Peer Architecture . . . . . . . . . . . . . . . . . . . . . . . 26 2.2.4 CAP Theorem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.2.5 Distributed ledger technology . . . . . . . . . . . . . . . . . . . . . 28 2.3 Cryptography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.3.2.4 Blockchain Fundamentals . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 2.4.1 Dening blockchain . . . . . . . . . . . . . . . . . . . . . . . . . . 35 2.4.2 Types of Blockchain . . . . . . . . . . . . . . . . . . . . . . . . . . 35 2.4.3 Blockchain architecture . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.4.4 Blockchain Core Components . . . . . . . . . . . . . . . . . . . . . 38 2.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 3 Project Overview and Requirements 44 3.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 3.1.1 Vendor Lock-in . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 3.1.2 Single Points of Failure . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.1.3 Cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.1.4 Privacy Concerns . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.1.5 Censorship and Control . . . . . . . . . . . . . . . . . . . . . . . . 46 3.1.6 Underutilized Resources . . . . . . . . . . . . . . . . . . . . . . . . 46 3.2 Product Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 3.2.1 General Concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.2.2 System Architecture Overview . . . . . . . . . . . . . . . . . . . . . 47 3.2.3 Core Vision and Objectives . . . . . . . . . . . . . . . . . . . . . . 49 3.2.4 Long-term Perspective . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.3 Development Roadmap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.3.1 Phase 1: Core Infrastructure (MVP) . . . . . . . . . . . . . . . . . 50 3.3.2 Phase 2: Expanding Services . . . . . . . . . . . . . . . . . . . . . . 50 3.3.3 Phase 3: Enhancements and Scalability . . . . . . . . . . . . . . . . 51 3.4 Software Development Process . . . . . . . . . . . . . . . . . . . . . . . . . 52 3.4.1 Methodology Overview . . . . . . . . . . . . . . . . . . . . . . . . . 52 3.4.2 Scrum for Planning and Roadmap Management . . . . . . . . . . . 52 3.4.3 XP for Fast Iteration and Technical Excellence . . . . . . . . . . . . 52 3.4.4 Rationale for the Hybrid Approach . . . . . . . . . . . . . . . . . . 53 3.5 Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 3.5.1 Business Requirements . . . . . . . . . . . . . . . . . . . . . . . . . 53 3.5.2 User Requirements (User Stories) . . . . . . . . . . . . . . . . . . . 54 3.5.3 Functional Requirements . . . . . . . . . . . . . . . . . . . . . . . . 54 3.5.4 Quality Attributes . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 3.6 Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 3.6.1 Global Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 3.6.2 Container Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 1 Methods of cryptography . . . . . . . . . . . . . . . . . . . . . . . 31 3.6.3 Use Case Diagrams . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 3.6.4 Sequence Diagrams . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 3.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 4 Implementation and Realization 67 4.1 Technology Stack Overview . . . . . . . . . . . . . . . . . . . . . . . . . . 67 4.2 Blockchain Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 4.2.1 Node.js & libp2p for Network Layer . . . . . . . . . . . . . . . . . . 69 4.2.2 Consensus Mechanism and Block Creation . . . . . . . . . . . . . . 69 4.2.3 Transaction Structure and Proof Verication . . . . . . . . . . . . . 70 4.3 Service Network Implementations . . . . . . . . . . . . . . . . . . . . . . . 71 4.3.1 File Hosting (IPFS & IPFS Cluster) . . . . . . . . . . . . . . . . . 71 4.3.2 Virtual Machines (Container-based) . . . . . . . . . . . . . . . . . . 72 4.3.3 Decentralized Databases (OrbitDB) . . . . . . . . . . . . . . . . . . 74 4.4 Provider Node Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . 75 4.5 Archee Web Application (Next.js) . . . . . . . . . . . . . . . . . . . . . . . 75 4.5.1 Full-Stack Architecture with Next.js . . . . . . . . . . . . . . . . . 76 4.5.2 Frontend Technologies and UI/UX . . . . . . . . . . . . . . . . . . 76 4.5.3 Data Management and Interaction . . . . . . . . . . . . . . . . . . 77 4.6 User Interface (UI) Screens . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 4.6.1 Homepage (Guest View) . . . . . . . . . . . . . . . . . . . . . . . . 78 4.6.2 Blog . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 4.6.3 Docs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 4.6.4 Provider Guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 4.6.5 Consumer Dashboard . . . . . . . . . . . . . . . . . . . . . . . . . . 83 4.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 A The Start-up Project 93 Côte titre : MAI/1070 Blockchain-Based Decentralized Cloud Platform [document électronique] / Abdelhakim Tebbi ; Goudjil, Lakhdar, Directeur de thèse . - [S.l.] : Setif:UFA, 2025 . - 1 vol (109 f .) ; 29 cm. Langues : Anglais (eng) Catégories : Thèses & Mémoires:Informatique Mots-clés : Blockchain Decentralized Cloud InterPlanetary File System (IPFS) Peer-to-Peer Network Data Sovereignty Distributed Storage Trustless Systems Decen- tralized Infrastructure Cloud Security Index. décimale : 004 Informatique Résumé : Cloud computing has become an essential element of contemporary digital infrastructure, facilitating scalable storage and computational services. However, conventional cloud systems are inherently centralized, which has raised concerns about data control, single points of failure, high costs and the concentration of power among a few large providers. In response to these concerns, this thesis proposes a blockchain-based decentralized cloud infrastructure that creates a peer-to-peer marketplace where individuals can monetize their unused computing and storage resources. The system enables resource providers to join a global network and oer various cloud services through a simple setup process, while users can access these services similar to traditional cloud platforms. The architecture integrates blockchain technology with distributed computing princi- ples to create a trustless environment where service delivery is veried and payments are automated. The blockchain serves as both a verication layer for ensuring services are properly delivered and as a transparent record of all transactions within the network. The system is exemplied through a functional MVP that demonstrates its primary functionalities. Although performance and scalability remain areas for future enhance- ments, this work underscores the viability of decentralized cloud solutions suitable for privacy-sensitive, cost eective and censorship-resistant applications in both personal and organizational settings. Note de contenu : Sommaire Abstract General Introduction 1 1 Cloud Computing 3 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2 Cloud Computing Foundation . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2.1 Denition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2.2 History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2.3 Cloud Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.2.4 Service Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.2.5 Cloud Deployment Models . . . . . . . . . . . . . . . . . . . . . . . 13 1.2.6 Cloud Enabling Technology . . . . . . . . . . . . . . . . . . . . . . 15 1.3 Cloud Computing Challenges . . . . . . . . . . . . . . . . . . . . . . . . . 20 1.3.1 Security Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 1.3.2 Centralized Control . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 1.3.3 Data sovereignty . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2 Blockchain 23 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.2 Distributed Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.2.1 Denition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.2.2 Distributed database system . . . . . . . . . . . . . . . . . . . . . 24 2.2.3 Peer to Peer Architecture . . . . . . . . . . . . . . . . . . . . . . . 26 2.2.4 CAP Theorem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.2.5 Distributed ledger technology . . . . . . . . . . . . . . . . . . . . . 28 2.3 Cryptography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.3.2.4 Blockchain Fundamentals . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 2.4.1 Dening blockchain . . . . . . . . . . . . . . . . . . . . . . . . . . 35 2.4.2 Types of Blockchain . . . . . . . . . . . . . . . . . . . . . . . . . . 35 2.4.3 Blockchain architecture . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.4.4 Blockchain Core Components . . . . . . . . . . . . . . . . . . . . . 38 2.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 3 Project Overview and Requirements 44 3.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 3.1.1 Vendor Lock-in . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 3.1.2 Single Points of Failure . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.1.3 Cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.1.4 Privacy Concerns . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.1.5 Censorship and Control . . . . . . . . . . . . . . . . . . . . . . . . 46 3.1.6 Underutilized Resources . . . . . . . . . . . . . . . . . . . . . . . . 46 3.2 Product Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 3.2.1 General Concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.2.2 System Architecture Overview . . . . . . . . . . . . . . . . . . . . . 47 3.2.3 Core Vision and Objectives . . . . . . . . . . . . . . . . . . . . . . 49 3.2.4 Long-term Perspective . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.3 Development Roadmap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.3.1 Phase 1: Core Infrastructure (MVP) . . . . . . . . . . . . . . . . . 50 3.3.2 Phase 2: Expanding Services . . . . . . . . . . . . . . . . . . . . . . 50 3.3.3 Phase 3: Enhancements and Scalability . . . . . . . . . . . . . . . . 51 3.4 Software Development Process . . . . . . . . . . . . . . . . . . . . . . . . . 52 3.4.1 Methodology Overview . . . . . . . . . . . . . . . . . . . . . . . . . 52 3.4.2 Scrum for Planning and Roadmap Management . . . . . . . . . . . 52 3.4.3 XP for Fast Iteration and Technical Excellence . . . . . . . . . . . . 52 3.4.4 Rationale for the Hybrid Approach . . . . . . . . . . . . . . . . . . 53 3.5 Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 3.5.1 Business Requirements . . . . . . . . . . . . . . . . . . . . . . . . . 53 3.5.2 User Requirements (User Stories) . . . . . . . . . . . . . . . . . . . 54 3.5.3 Functional Requirements . . . . . . . . . . . . . . . . . . . . . . . . 54 3.5.4 Quality Attributes . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 3.6 Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 3.6.1 Global Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 3.6.2 Container Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 1 Methods of cryptography . . . . . . . . . . . . . . . . . . . . . . . 31 3.6.3 Use Case Diagrams . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 3.6.4 Sequence Diagrams . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 3.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 4 Implementation and Realization 67 4.1 Technology Stack Overview . . . . . . . . . . . . . . . . . . . . . . . . . . 67 4.2 Blockchain Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 4.2.1 Node.js & libp2p for Network Layer . . . . . . . . . . . . . . . . . . 69 4.2.2 Consensus Mechanism and Block Creation . . . . . . . . . . . . . . 69 4.2.3 Transaction Structure and Proof Verication . . . . . . . . . . . . . 70 4.3 Service Network Implementations . . . . . . . . . . . . . . . . . . . . . . . 71 4.3.1 File Hosting (IPFS & IPFS Cluster) . . . . . . . . . . . . . . . . . 71 4.3.2 Virtual Machines (Container-based) . . . . . . . . . . . . . . . . . . 72 4.3.3 Decentralized Databases (OrbitDB) . . . . . . . . . . . . . . . . . . 74 4.4 Provider Node Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . 75 4.5 Archee Web Application (Next.js) . . . . . . . . . . . . . . . . . . . . . . . 75 4.5.1 Full-Stack Architecture with Next.js . . . . . . . . . . . . . . . . . 76 4.5.2 Frontend Technologies and UI/UX . . . . . . . . . . . . . . . . . . 76 4.5.3 Data Management and Interaction . . . . . . . . . . . . . . . . . . 77 4.6 User Interface (UI) Screens . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 4.6.1 Homepage (Guest View) . . . . . . . . . . . . . . . . . . . . . . . . 78 4.6.2 Blog . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 4.6.3 Docs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 4.6.4 Provider Guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 4.6.5 Consumer Dashboard . . . . . . . . . . . . . . . . . . . . . . . . . . 83 4.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 A The Start-up Project 93 Côte titre : MAI/1070

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
MAI/1070	MAI/1070	Mémoire	Bibliothèque des sciences	Anglais	Disponible Disponible

Blockchain-based deep learning to improve the security of the Industrial Internet of Things / Aya Bendjedi

Public ISBD Titre : Blockchain-based deep learning to improve the security of the Industrial Internet of Things Type de document : texte imprimé Auteurs : Aya Bendjedi, Auteur ; Chems Zerguine ; Goudjil, Lakhdar, Directeur de thèse Editeur : Setif:UFA Année de publication : 2024 Importance : 1 vol (99 f .) Format : 29 cm Langues : Anglais (eng) Catégories : Thèses & Mémoires:Informatique Mots-clés : IIoT Blockchain BIIoT Deep Learning X-IIOTID WUSTL-IIoT-2021 Edge- IIoTset. Index. décimale : 004 - Informatique Résumé : The integration of blockchain development of advanced solutions for se- and deep learning technologies holds signi- cure data exchange, anomaly detection, and ficant promise in enhancing the security of threat mitigation, we aim to bolster the se- Industrial Internet of Things (IIoT) systems. curity of IIoT networks. Prototype systems This paper explores the potential of leveraging are designed and implemented to showcaseblockchain- based deep learning approaches to the practical application of blockchain-based address the unique security challenges faced deep learning in IIoT security, with evalua- by IIoT deployments. We propose novel ar- tions conducted in real-world industrial set- chitectures and algorithms tailored to seam- tings using the aforementioned datasets. Our lessly integrate blockchain and deep learning findings demonstrate the effectiveness of inte- techniques into IIoT environments, focusing grating blockchain and deep learning metho- on datasets representative of Edge IIoTset, dologies, highlighting improvements in secu- WUSTL-IIoT- 2021, and X-IIoTID. Through the rity, accuracy, and scalability in IIoT environ- ments. Note de contenu : Sommaire General Introduction 1 0.1 Genral Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 0.1.1 Context and Motivation . . . . . . . . . . . . . . . . . . . . . . . . 1 0.1.2 Objectives and Contributions . . . . . . . . . . . . . . . . . . . . . 1 0.1.3 Organization of the Manuscript . . . . . . . . . . . . . . . . . . . . 2 1 the Basic Concepts of IoT and IIoT 3 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2 Internet of Things . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 IoT History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.4 IoT Architecture Layers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.4.1 Three-Layer Architecture . . . . . . . . . . . . . . . . . . . . . . . 6 1.4.2 Five-Layer Architecture . . . . . . . . . . . . . . . . . . . . . . . . 6 1.5 Features of IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.6 Domains of Internet of Things . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.6.1 Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.6.2 Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.6.3 Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.6.4 Healthcare . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.7 Benefits of IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.8 Disadvantages of IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.9 Definition of IIoT .............................................................................................. 10 1.10 Transformation of IoT to IIoT ............................................................................ 10 1.11 Milestones of IIoT ............................................................................................. 10 1.12 IIoT challenges .................................................................................................. 11 1.13 IIoT Architecture ............................................................................................... 12 1.14 Industrial IoT and Industry 4.0 ........................................................................... 13 1.15 Foundations of the IIoT ..................................................................................... 14 1.16 The Scope of Application of the IIoT................................................................. 14 1.17 Conclusion ........................................................................................................ 14 2 Blockchain in Industrial Internet of Things (IIoT) 17 2.1 Introduction ...................................................................................................... 18 2.2 Definition of Blockchain ................................................................................... 18 2.3 History of Blockchain ....................................................................................... 18 2.4 Blockchain Functionalities and Implications ..................................................... 19 2.5 Applications of Blockchain ............................................................................... 20 2.6 Blockchain Key Characteristics......................................................................... 20 2.7 Types and Taxonomy of Blockchain Systems .................................................... 22 2.7.1 Types of Blockchain ............................................................................. 22 2.7.2 Comparison of Blockchain Types ......................................................... 22 2.8 Blockchain Infrastructure .................................................................................. 23 2.9 Working Of a Blockchain.................................................................................. 26 2.10 Blockchain -IIOT Challenges ............................................................................ 26 2.11 Potential Solutions to Blockchain-IIoT Challenges ........................................... 27 2.12 Chances of combining IIoT with blockchain ..................................................... 28 2.13 Architecture of BIIoT ........................................................................................ 29 2.14 BIIoT applications ............................................................................................ 30 2.14.1 Food sector ........................................................................................... 31 2.14.2 Smart manufacturing sector .................................................................. 31 2.14.3 Healthcare sector .................................................................................. 31 2.14.4 Automotive Industry ............................................................................. 31 2.14.5 Oil and gas sector.................................................................................. 32 2.14.6 Trade supply chain industry ................................................................ 32 2.15 BIIoT open research issues ................................................................................ 32 2.15.1 Privacy leakage ..................................................................................... 33 2.15.2 Security vulnerability ............................................................................ 33 2.15.3 Constraints of resources ........................................................................ 33 2.15.4 Scalability............................................................................................. 34 2.15.5 Big data difficulty ................................................................................. 34 2.16 Conclusion ........................................................................................................ 35 3 Machine Learning and Deep Learning 37 3.1 Introduction ...................................................................................................... 38 3.2 Definition of Machine Learning ........................................................................ 38 3.3 Renaissance of Machine Learning .................................................................... 38 3.4 Types of Machine Learning ............................................................................... 39 3.4.1 Supervised Learning ............................................................................. 39 3.4.2 Unsupervised Learning ......................................................................... 40 3.4.3 Semi-Supervised Learning .................................................................... 41 3.4.4 Reinforcement Learning ....................................................................... 41 3.5 Deep Learning .................................................................................................. 43 3.6 Layers of a neural network ................................................................................ 44 3.7 Activation Functions ......................................................................................... 45 3.8 Machine Learning vs Deep Learning................................................................. 45 3.9 Applications of Deep Learning .......................................................................... 46 3.10 Areas of Deep Learning System ........................................................................ 46 3.11 Deep Learning Architectures ............................................................................ 47 3.11.1 Convolutional neural networks (CNNs) ................................................ 47 3.11.2 Recurrent neural networks (RNNs) ....................................................... 47 3.11.3 Deep Neural Network (DNN) ................................................................ 48 3.12 DEEP LEARNING FOR THE IIOT ............................................................................ 48 3.13 Introduction ....................................................................................................... 50 3.14 Blockchain-based deep learning ........................................................................ 51 3.15 Blockchain-based Deep learning Application areas ........................................... 53 3.15.1 Healthcare ............................................................................................. 53 3.15.2 Internet of vehicles ................................................................................ 53 3.15.3 Traffic management .............................................................................. 54 3.15.4 Safety and protection............................................................................. 54 3.16 Blockchain-based Deep learning Services .......................................................... 54 3.16.1 Privacy preservation ............................................................................. 55 3.16.2 Violation prediction .............................................................................. 55 3.16.3 Anomaly detection ................................................................................ 55 3.16.4 Data traffic management ....................................................................... 55 3.16.5 Forking prevention ............................................................................... 55 3.16.6 EHR forecasting .................................................................................... 56 3.17 Blockchain-based Deep learningData types ....................................................... 56 3.17.1 Image data CNN.................................................................................... 56 3.17.2 Textual data Textual .............................................................................. 56 3.18 Blockchain-based Deep learning Deployment goal............................................ 56 3.18.1 Trusted AI models ................................................................................. 57 3.18.2 AI decisions sharing .............................................................................. 57 3.19 Conclusion ........................................................................................................ 58 4 Blockchain-Based Deep Learning to Improve the Security of the Industrial Internet of Things 59 4.1 Introduction ....................................................................................................... 63 4.2 A Novel Privacy-Preserving and Secure Framework (PPSS) for Industry 4.0/5.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.2.1 Definition and Proposed Method........................................................... 63 4.3 Integrating Deep Learning and Blockchain to Secure Industrial IoT Net- works from Cyberattacks .................................................................................. 63 4.3.1 Definition of intrusion detection system (IDS) ....................................... 63 4.4 Differences and similarities ............................................................................... 64 4.5 Dataset in IIoT................................................................................................... 65 4.6 Description of Edge-IIoTset ........................................................................................ 68 4.7 Attacks EDGE-IIoTset Dataset .................................................................................... 68 4.7.1 Attaques DoS/DDoS ............................................................................. 68 4.7.2 Information Collection .......................................................................... 68 4.8 Man in the middle attack .................................................................................. 69 4.9 Malware Attacks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 4.10 Description of WUSTL-IIoT-2021 . . . . . . . . . . . . . . . . . . . . . . . . 71 4.11 Attacks in WUSTL-IIoT-2021 Dataset . . . . . . . . . . . . . . . . . . . . . 71 4.11.1 Denial-of-Service (DoS) Attacks . . . . . . . . . . . . . . . . . . . . . . . . . 71 4.11.2 Reconnaissance Attacks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 4.11.3 Command Injection Attacks . . . . . . . . . . . . . . . . . . . . . . . 71 4.11.4 Backdoor Attacks . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . 71 4.12 Description of X-IIoTID . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 4.13 Number of Classes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 4.13.1 Class 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 4.13.2 Class 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 4.13.3 Class 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 4.14 Attacks in X-IIoTID . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 4.14.1 Denial-of-Service (DoS) Attacks . . . . . . . . . . . . . . . . . . . . . . . . . 73 4.14.2ScanningAttacks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 4.14.3 Brute-Force Attacks (Attaque BruteForce) . . . . . . . . . . . . . . 74 4.14.4 MQTT_cloud_broker_subscription Attack . . . . . . . . . . . . . . 74 4.14.5 Discovering_resources Attack . . . . . . . . . . . . . . . . . . . . . 74 4.14.6 Exfiltration Attack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 4.14.7 Insider_malicious Attack . . . . . . . . . . . . . . . . . . . . . . . . 74 4.14.8 Modbus_register_reading Attack . . . . . . . . . . . . . . . . . . . 74 4.14.9 False_data_injection Attack . . . . . . . . . . . . . . . . . . . . . . 74 4.14.10 Command and Control (C&C) Attack . . . . . . . . . . . . . . . . . 75 4.14.11 Dictionary Attack (Attaque Dictionary) . . . . . . . . . . . . . . . 75 4.14.12 TCP Relay Attack (Attaque TCP Relay) . . . . . . . . . . . . . . . . 75 4.14.13 Fuzzing Attack (Attaque fuzzing) . . . . . . . . . . . . . . . . . . . 75 4.14.14 Reverse_shell Attack . . . . . . . . . . . . . . . . . . . . . . . . . . 75 4.14.15 Man-in-the-Middle (MitM) Attack . . . . . . . . . . . . . . . . . . . . . . . . . . 75 4.14.16 Fake Notification Attack . . . . . . . . . . . . . . . . . . . . . . . . 75 4.14.17 Cryptoransomware Attack . . . . . . . . . . . . . . . . . . . . . . . 75 4.15 Visual Display for Three Different Kinds of Datasets . . . . . . . . . . . . . 79 4.15.1 Heatmap Correlation of Numerical Features . . . . . . . . . . . . . 79 4.16 Top Attack Types Displayed in a Bar Chart . . . . . . . . . . . . . . . . . . 81 4.17 Distribution of Attack Types in the Dataset . . . . . . . . . . . . . . . . . . 83 4.17.1 1) Distribution of Attack Types in the Edge-IIoT Dataset . . . . . . 83 4.17.2 Distribution of Attack Types in the X-IIOTID Dataset . . . . . . . . 84 4.17.3 Exploring Data Distribution through Histograms . . . . . . . . . . 87 4.18 Application of DNN Models in IIoT Datasets . . . . . . . . . . . . . . . . . 90 4.18.1 Classification Report of the Model in Dataset . . . . . . . . . . . . 91 4.19 Integrate Blockchain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 4.19.1 Why Integrate Blockchain . . . . . . . . . . . . . . . . . . . . . . . 94 4.19.2 How to Integrate Blockchain with a Deep Learning Model . . . . . 94 4.19.3 Blockchain details in Edge-IIoT . . . . . . . . . . . . . . . . . . . . 95 4.19.4 Blockchain details in X-IIOTID . . . . . . . . . . . . . . . . . . . . 95 4.19.5 Blockchain details in Wustl-IIoT . . . . . . . . . . . . . . . . . . . 964.19.6 Accuracy ............................................................................................... 96 4.19.7 Macro Average ..................................................................................... 96 4.19.8 Weighted Average ................................................................................. 97 4.20 Conclusion ........................................................................................................ 97 Côte titre : MAI/0925 Blockchain-based deep learning to improve the security of the Industrial Internet of Things [texte imprimé] / Aya Bendjedi, Auteur ; Chems Zerguine ; Goudjil, Lakhdar, Directeur de thèse . - [S.l.] : Setif:UFA, 2024 . - 1 vol (99 f .) ; 29 cm. Langues : Anglais (eng) Catégories : Thèses & Mémoires:Informatique Mots-clés : IIoT Blockchain BIIoT Deep Learning X-IIOTID WUSTL-IIoT-2021 Edge- IIoTset. Index. décimale : 004 - Informatique Résumé : The integration of blockchain development of advanced solutions for se- and deep learning technologies holds signi- cure data exchange, anomaly detection, and ficant promise in enhancing the security of threat mitigation, we aim to bolster the se- Industrial Internet of Things (IIoT) systems. curity of IIoT networks. Prototype systems This paper explores the potential of leveraging are designed and implemented to showcaseblockchain- based deep learning approaches to the practical application of blockchain-based address the unique security challenges faced deep learning in IIoT security, with evalua- by IIoT deployments. We propose novel ar- tions conducted in real-world industrial set- chitectures and algorithms tailored to seam- tings using the aforementioned datasets. Our lessly integrate blockchain and deep learning findings demonstrate the effectiveness of inte- techniques into IIoT environments, focusing grating blockchain and deep learning metho- on datasets representative of Edge IIoTset, dologies, highlighting improvements in secu- WUSTL-IIoT- 2021, and X-IIoTID. Through the rity, accuracy, and scalability in IIoT environ- ments. Note de contenu : Sommaire General Introduction 1 0.1 Genral Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 0.1.1 Context and Motivation . . . . . . . . . . . . . . . . . . . . . . . . 1 0.1.2 Objectives and Contributions . . . . . . . . . . . . . . . . . . . . . 1 0.1.3 Organization of the Manuscript . . . . . . . . . . . . . . . . . . . . 2 1 the Basic Concepts of IoT and IIoT 3 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2 Internet of Things . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 IoT History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.4 IoT Architecture Layers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.4.1 Three-Layer Architecture . . . . . . . . . . . . . . . . . . . . . . . 6 1.4.2 Five-Layer Architecture . . . . . . . . . . . . . . . . . . . . . . . . 6 1.5 Features of IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.6 Domains of Internet of Things . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.6.1 Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.6.2 Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.6.3 Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.6.4 Healthcare . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.7 Benefits of IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.8 Disadvantages of IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.9 Definition of IIoT .............................................................................................. 10 1.10 Transformation of IoT to IIoT ............................................................................ 10 1.11 Milestones of IIoT ............................................................................................. 10 1.12 IIoT challenges .................................................................................................. 11 1.13 IIoT Architecture ............................................................................................... 12 1.14 Industrial IoT and Industry 4.0 ........................................................................... 13 1.15 Foundations of the IIoT ..................................................................................... 14 1.16 The Scope of Application of the IIoT................................................................. 14 1.17 Conclusion ........................................................................................................ 14 2 Blockchain in Industrial Internet of Things (IIoT) 17 2.1 Introduction ...................................................................................................... 18 2.2 Definition of Blockchain ................................................................................... 18 2.3 History of Blockchain ....................................................................................... 18 2.4 Blockchain Functionalities and Implications ..................................................... 19 2.5 Applications of Blockchain ............................................................................... 20 2.6 Blockchain Key Characteristics......................................................................... 20 2.7 Types and Taxonomy of Blockchain Systems .................................................... 22 2.7.1 Types of Blockchain ............................................................................. 22 2.7.2 Comparison of Blockchain Types ......................................................... 22 2.8 Blockchain Infrastructure .................................................................................. 23 2.9 Working Of a Blockchain.................................................................................. 26 2.10 Blockchain -IIOT Challenges ............................................................................ 26 2.11 Potential Solutions to Blockchain-IIoT Challenges ........................................... 27 2.12 Chances of combining IIoT with blockchain ..................................................... 28 2.13 Architecture of BIIoT ........................................................................................ 29 2.14 BIIoT applications ............................................................................................ 30 2.14.1 Food sector ........................................................................................... 31 2.14.2 Smart manufacturing sector .................................................................. 31 2.14.3 Healthcare sector .................................................................................. 31 2.14.4 Automotive Industry ............................................................................. 31 2.14.5 Oil and gas sector.................................................................................. 32 2.14.6 Trade supply chain industry ................................................................ 32 2.15 BIIoT open research issues ................................................................................ 32 2.15.1 Privacy leakage ..................................................................................... 33 2.15.2 Security vulnerability ............................................................................ 33 2.15.3 Constraints of resources ........................................................................ 33 2.15.4 Scalability............................................................................................. 34 2.15.5 Big data difficulty ................................................................................. 34 2.16 Conclusion ........................................................................................................ 35 3 Machine Learning and Deep Learning 37 3.1 Introduction ...................................................................................................... 38 3.2 Definition of Machine Learning ........................................................................ 38 3.3 Renaissance of Machine Learning .................................................................... 38 3.4 Types of Machine Learning ............................................................................... 39 3.4.1 Supervised Learning ............................................................................. 39 3.4.2 Unsupervised Learning ......................................................................... 40 3.4.3 Semi-Supervised Learning .................................................................... 41 3.4.4 Reinforcement Learning ....................................................................... 41 3.5 Deep Learning .................................................................................................. 43 3.6 Layers of a neural network ................................................................................ 44 3.7 Activation Functions ......................................................................................... 45 3.8 Machine Learning vs Deep Learning................................................................. 45 3.9 Applications of Deep Learning .......................................................................... 46 3.10 Areas of Deep Learning System ........................................................................ 46 3.11 Deep Learning Architectures ............................................................................ 47 3.11.1 Convolutional neural networks (CNNs) ................................................ 47 3.11.2 Recurrent neural networks (RNNs) ....................................................... 47 3.11.3 Deep Neural Network (DNN) ................................................................ 48 3.12 DEEP LEARNING FOR THE IIOT ............................................................................ 48 3.13 Introduction ....................................................................................................... 50 3.14 Blockchain-based deep learning ........................................................................ 51 3.15 Blockchain-based Deep learning Application areas ........................................... 53 3.15.1 Healthcare ............................................................................................. 53 3.15.2 Internet of vehicles ................................................................................ 53 3.15.3 Traffic management .............................................................................. 54 3.15.4 Safety and protection............................................................................. 54 3.16 Blockchain-based Deep learning Services .......................................................... 54 3.16.1 Privacy preservation ............................................................................. 55 3.16.2 Violation prediction .............................................................................. 55 3.16.3 Anomaly detection ................................................................................ 55 3.16.4 Data traffic management ....................................................................... 55 3.16.5 Forking prevention ............................................................................... 55 3.16.6 EHR forecasting .................................................................................... 56 3.17 Blockchain-based Deep learningData types ....................................................... 56 3.17.1 Image data CNN.................................................................................... 56 3.17.2 Textual data Textual .............................................................................. 56 3.18 Blockchain-based Deep learning Deployment goal............................................ 56 3.18.1 Trusted AI models ................................................................................. 57 3.18.2 AI decisions sharing .............................................................................. 57 3.19 Conclusion ........................................................................................................ 58 4 Blockchain-Based Deep Learning to Improve the Security of the Industrial Internet of Things 59 4.1 Introduction ....................................................................................................... 63 4.2 A Novel Privacy-Preserving and Secure Framework (PPSS) for Industry 4.0/5.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.2.1 Definition and Proposed Method........................................................... 63 4.3 Integrating Deep Learning and Blockchain to Secure Industrial IoT Net- works from Cyberattacks .................................................................................. 63 4.3.1 Definition of intrusion detection system (IDS) ....................................... 63 4.4 Differences and similarities ............................................................................... 64 4.5 Dataset in IIoT................................................................................................... 65 4.6 Description of Edge-IIoTset ........................................................................................ 68 4.7 Attacks EDGE-IIoTset Dataset .................................................................................... 68 4.7.1 Attaques DoS/DDoS ............................................................................. 68 4.7.2 Information Collection .......................................................................... 68 4.8 Man in the middle attack .................................................................................. 69 4.9 Malware Attacks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 4.10 Description of WUSTL-IIoT-2021 . . . . . . . . . . . . . . . . . . . . . . . . 71 4.11 Attacks in WUSTL-IIoT-2021 Dataset . . . . . . . . . . . . . . . . . . . . . 71 4.11.1 Denial-of-Service (DoS) Attacks . . . . . . . . . . . . . . . . . . . . . . . . . 71 4.11.2 Reconnaissance Attacks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 4.11.3 Command Injection Attacks . . . . . . . . . . . . . . . . . . . . . . . 71 4.11.4 Backdoor Attacks . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . 71 4.12 Description of X-IIoTID . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 4.13 Number of Classes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 4.13.1 Class 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 4.13.2 Class 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 4.13.3 Class 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 4.14 Attacks in X-IIoTID . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 4.14.1 Denial-of-Service (DoS) Attacks . . . . . . . . . . . . . . . . . . . . . . . . . 73 4.14.2ScanningAttacks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 4.14.3 Brute-Force Attacks (Attaque BruteForce) . . . . . . . . . . . . . . 74 4.14.4 MQTT_cloud_broker_subscription Attack . . . . . . . . . . . . . . 74 4.14.5 Discovering_resources Attack . . . . . . . . . . . . . . . . . . . . . 74 4.14.6 Exfiltration Attack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 4.14.7 Insider_malicious Attack . . . . . . . . . . . . . . . . . . . . . . . . 74 4.14.8 Modbus_register_reading Attack . . . . . . . . . . . . . . . . . . . 74 4.14.9 False_data_injection Attack . . . . . . . . . . . . . . . . . . . . . . 74 4.14.10 Command and Control (C&C) Attack . . . . . . . . . . . . . . . . . 75 4.14.11 Dictionary Attack (Attaque Dictionary) . . . . . . . . . . . . . . . 75 4.14.12 TCP Relay Attack (Attaque TCP Relay) . . . . . . . . . . . . . . . . 75 4.14.13 Fuzzing Attack (Attaque fuzzing) . . . . . . . . . . . . . . . . . . . 75 4.14.14 Reverse_shell Attack . . . . . . . . . . . . . . . . . . . . . . . . . . 75 4.14.15 Man-in-the-Middle (MitM) Attack . . . . . . . . . . . . . . . . . . . . . . . . . . 75 4.14.16 Fake Notification Attack . . . . . . . . . . . . . . . . . . . . . . . . 75 4.14.17 Cryptoransomware Attack . . . . . . . . . . . . . . . . . . . . . . . 75 4.15 Visual Display for Three Different Kinds of Datasets . . . . . . . . . . . . . 79 4.15.1 Heatmap Correlation of Numerical Features . . . . . . . . . . . . . 79 4.16 Top Attack Types Displayed in a Bar Chart . . . . . . . . . . . . . . . . . . 81 4.17 Distribution of Attack Types in the Dataset . . . . . . . . . . . . . . . . . . 83 4.17.1 1) Distribution of Attack Types in the Edge-IIoT Dataset . . . . . . 83 4.17.2 Distribution of Attack Types in the X-IIOTID Dataset . . . . . . . . 84 4.17.3 Exploring Data Distribution through Histograms . . . . . . . . . . 87 4.18 Application of DNN Models in IIoT Datasets . . . . . . . . . . . . . . . . . 90 4.18.1 Classification Report of the Model in Dataset . . . . . . . . . . . . 91 4.19 Integrate Blockchain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 4.19.1 Why Integrate Blockchain . . . . . . . . . . . . . . . . . . . . . . . 94 4.19.2 How to Integrate Blockchain with a Deep Learning Model . . . . . 94 4.19.3 Blockchain details in Edge-IIoT . . . . . . . . . . . . . . . . . . . . 95 4.19.4 Blockchain details in X-IIOTID . . . . . . . . . . . . . . . . . . . . 95 4.19.5 Blockchain details in Wustl-IIoT . . . . . . . . . . . . . . . . . . . 964.19.6 Accuracy ............................................................................................... 96 4.19.7 Macro Average ..................................................................................... 96 4.19.8 Weighted Average ................................................................................. 97 4.20 Conclusion ........................................................................................................ 97 Côte titre : MAI/0925

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
MAI/0925	MAI/0925	Mémoire	Bibliothèque des sciences	Anglais	Disponible Disponible

Blockchain technology applications for Industry 4.0 / Djaouida Benkila  

Public ISBD Titre : Blockchain technology applications for Industry 4.0 Type de document : texte imprimé Auteurs : Djaouida Benkila, Auteur ; Goudjil, Lakhdar, Directeur de thèse Editeur : Sétif:UFA1 Année de publication : 2023 Importance : 1 vol (77f .) Format : 29cm Langues : Anglais (eng) Catégories : Thèses & Mémoires:Informatique Mots-clés : Blockchain Industry 4.0 Index. décimale : 004 Informatique Résumé : Blockchain technology is transforming industries in the digital innovation landscape, including Industry 4.0 and the upcoming Industry 5.0. In Industry 4.0, blockchain enhances trust, security, and transparency by enabling secure record-keeping and decentralized networks. It streamlines operations and creates a more efficient ecosystem. In Industry 5.0, blockchain empowers individuals with control over digital identities, intellectual property, and financial transactions. Decentralized applications and smart contracts facilitate peer-to-peer interactions. Blockchain's impact extends beyond manufacturing to healthcare, finance, logistics, and energy, enhancing data sharing, cybersecurity, and trust. Its decentralized nature mitigates failures and safeguards sensitive information. In this study we Investigating the fundamentals of Industry 4.0, and industry 5.0 Its challenges, barriers, and limitations. Finally exploring areas where blockchain technology can bring new features and add value to the deployment of them. Côte titre : MAI/0714 En ligne : https://drive.google.com/file/d/1ugBW1k_VTIthmTVaE-B6zFGDTYfuTYKP/view?usp=drive [...] Format de la ressource électronique : pdf Blockchain technology applications for Industry 4.0 [texte imprimé] / Djaouida Benkila, Auteur ; Goudjil, Lakhdar, Directeur de thèse . - [S.l.] : Sétif:UFA1, 2023 . - 1 vol (77f .) ; 29cm. Langues : Anglais (eng) Catégories : Thèses & Mémoires:Informatique Mots-clés : Blockchain Industry 4.0 Index. décimale : 004 Informatique Résumé : Blockchain technology is transforming industries in the digital innovation landscape, including Industry 4.0 and the upcoming Industry 5.0. In Industry 4.0, blockchain enhances trust, security, and transparency by enabling secure record-keeping and decentralized networks. It streamlines operations and creates a more efficient ecosystem. In Industry 5.0, blockchain empowers individuals with control over digital identities, intellectual property, and financial transactions. Decentralized applications and smart contracts facilitate peer-to-peer interactions. Blockchain's impact extends beyond manufacturing to healthcare, finance, logistics, and energy, enhancing data sharing, cybersecurity, and trust. Its decentralized nature mitigates failures and safeguards sensitive information. In this study we Investigating the fundamentals of Industry 4.0, and industry 5.0 Its challenges, barriers, and limitations. Finally exploring areas where blockchain technology can bring new features and add value to the deployment of them. Côte titre : MAI/0714 En ligne : https://drive.google.com/file/d/1ugBW1k_VTIthmTVaE-B6zFGDTYfuTYKP/view?usp=drive [...] Format de la ressource électronique : pdf

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
MAI/0714	MAI/0714	Mémoire	Bibliothèque des sciences	Anglais	Disponible Disponible

Enhancing IoT Security For Smart Homes Through blockchain / Hichem Maazouz

Public ISBD Titre : Enhancing IoT Security For Smart Homes Through blockchain Type de document : texte imprimé Auteurs : Hichem Maazouz, Auteur ; Haithem Berhail ; Goudjil, Lakhdar, Directeur de thèse Editeur : Setif:UFA Année de publication : 2024 Importance : 1 vol (80 f .) Format : 29 cm Langues : Anglais (eng) Catégories : Thèses & Mémoires:Informatique Mots-clés : Informatique Enhancing IoT Security Index. décimale : 004 - Informatique Résumé : Integrating Internet of Things (IoT) technology into smart homes significantly advances home automation and security. However, the proliferation of IoT devices introduces complex security vulnerabilities. Blockchain technology, with its decentralized and secure nature, offers a promising solution. This thesis introduces HomeChain, a blockchain-based security framework for smart homes, enhancing data integrity, privacy, and access control in IoT networks. We analyze HomeChain’s architecture and functionality and conduct a performance evaluation focusing on its effectiveness in mitigating common smart home security threats. The results demonstrate blockchain’s potential to improve security and operational efficiency in smart home IoT systems, positioning it as a leading candidate for standardizing smart home security solutions. Note de contenu : Sommaire GENERAL INTRODUCTION 1 Chapter1: IoT AND SMART HOME 2 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Architecture of IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3.1 Three layer Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3.2 Five-Layer Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3.3 Middle Ware Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3.4 SoA Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.4 IoT layers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.4.1 Perception layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.4.2 Network layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.4.3 Application Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.5 Application of IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.5.1 Smart Homes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.5.2 Healthcare . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.5.3 Smart industrial . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.5.4 Smart Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.6 IoT Communication Protocols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.6.1 MQTT (Message Queue Telemetry Transport) . . . . . . . . . . . . . . . . 7 1.6.2 CoAP (Constrained Application Protocol) . . . . . . . . . . . . . . . . . . . 7 1.6.3 Extensible Messaging and Presence Protocol (XMPP) . . . . . . . . . . . . 8 1.7 Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.8 Main Issues and Challenges of IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.8.1 Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.8.2 Privacy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.8.3 Interoperability and standards . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.8.4 Regulatory and rights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.9 Smart Home . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.10 How smart home works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 1.11 Different areas of application of smart home . . . . . . . . . . . . . . . . . . . . . . 13 1.11.1 Smart home for comfort . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1.11.2 Smart home for energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1.11.3 Smart home for security . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 1.11.4 Smart home for health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 1.12 Components of Smart Homes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.12.1 Smart objects/Smart devices . . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.12.2 Hubs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.13 Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 1.13.1 Presentation of the sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 1.14 Actuators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 1.15 Connecting house . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.15.1 Wireless smart home . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.16 Smart home management systems . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 1.16.1 Cloud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 1.16.2 Third parties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 1.17 Advantages and disadvantages of Smart home . . . . . . . . . . . . . . . . . . . . . 21 1.17.1 Advantages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 1.17.2 Disadvantages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 1.18 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Chapter2: SECURITY IN SMART HOME 22 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.2 Fundamentals of a Secure System . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.2.1 Confidentiality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.2.2 Integrity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.2.3 Availability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.2.4 Privacy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.3 Types of Smart Home Security Threats and Vulnerabilities in Smart Home . . . . . . 24 2.3.1 Unintentional Threats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.3.2 Malfunctions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.3.3 Intentional threats/abuse . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.4 Strategies for Mitigating Security Attacks in Smart Home Systems . . . . . . . . . . 27 2.4.1 Fighting against phishing . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.4.2 Malicious code detection . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.4.3 Tamper resistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.4.4 Security against eavesdropping . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.4.5 Snifng detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.4.6 Network monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.4.7 Secure key management . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.4.8 Physical protection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.5 Strong Security Protocols and Best Practices for Smart Home systems . . . . . . . . 29 2.5.1 Network Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.5.2 Strong Authentication Methods . . . . . . . . . . . . . . . . . . . . . . . . 29 2.5.3 Encryption Protocols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.5.4 Regular Security Audits . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.5.5 Device Firmware Integrity Verification . . . . . . . . . . . . . . . . . . . . 30 2.5.6 Intrusion Detection Systems (IDS) . . . . . . . . . . . . . . . . . . . . . . . 30 2.5.7 Blockchain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.6 Standards and Regulations for smarthome . . . . . . . . . . . . . . . . . . . . . . . 31 .......... Côte titre : MAI/0852 Enhancing IoT Security For Smart Homes Through blockchain [texte imprimé] / Hichem Maazouz, Auteur ; Haithem Berhail ; Goudjil, Lakhdar, Directeur de thèse . - [S.l.] : Setif:UFA, 2024 . - 1 vol (80 f .) ; 29 cm. Langues : Anglais (eng) Catégories : Thèses & Mémoires:Informatique Mots-clés : Informatique Enhancing IoT Security Index. décimale : 004 - Informatique Résumé : Integrating Internet of Things (IoT) technology into smart homes significantly advances home automation and security. However, the proliferation of IoT devices introduces complex security vulnerabilities. Blockchain technology, with its decentralized and secure nature, offers a promising solution. This thesis introduces HomeChain, a blockchain-based security framework for smart homes, enhancing data integrity, privacy, and access control in IoT networks. We analyze HomeChain’s architecture and functionality and conduct a performance evaluation focusing on its effectiveness in mitigating common smart home security threats. The results demonstrate blockchain’s potential to improve security and operational efficiency in smart home IoT systems, positioning it as a leading candidate for standardizing smart home security solutions. Note de contenu : Sommaire GENERAL INTRODUCTION 1 Chapter1: IoT AND SMART HOME 2 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Architecture of IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3.1 Three layer Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3.2 Five-Layer Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3.3 Middle Ware Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3.4 SoA Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.4 IoT layers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.4.1 Perception layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.4.2 Network layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.4.3 Application Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.5 Application of IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.5.1 Smart Homes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.5.2 Healthcare . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.5.3 Smart industrial . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.5.4 Smart Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.6 IoT Communication Protocols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.6.1 MQTT (Message Queue Telemetry Transport) . . . . . . . . . . . . . . . . 7 1.6.2 CoAP (Constrained Application Protocol) . . . . . . . . . . . . . . . . . . . 7 1.6.3 Extensible Messaging and Presence Protocol (XMPP) . . . . . . . . . . . . 8 1.7 Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.8 Main Issues and Challenges of IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.8.1 Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.8.2 Privacy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.8.3 Interoperability and standards . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.8.4 Regulatory and rights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.9 Smart Home . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.10 How smart home works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 1.11 Different areas of application of smart home . . . . . . . . . . . . . . . . . . . . . . 13 1.11.1 Smart home for comfort . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1.11.2 Smart home for energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1.11.3 Smart home for security . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 1.11.4 Smart home for health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 1.12 Components of Smart Homes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.12.1 Smart objects/Smart devices . . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.12.2 Hubs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.13 Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 1.13.1 Presentation of the sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 1.14 Actuators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 1.15 Connecting house . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.15.1 Wireless smart home . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.16 Smart home management systems . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 1.16.1 Cloud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 1.16.2 Third parties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 1.17 Advantages and disadvantages of Smart home . . . . . . . . . . . . . . . . . . . . . 21 1.17.1 Advantages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 1.17.2 Disadvantages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 1.18 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Chapter2: SECURITY IN SMART HOME 22 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.2 Fundamentals of a Secure System . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.2.1 Confidentiality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.2.2 Integrity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.2.3 Availability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.2.4 Privacy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.3 Types of Smart Home Security Threats and Vulnerabilities in Smart Home . . . . . . 24 2.3.1 Unintentional Threats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.3.2 Malfunctions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.3.3 Intentional threats/abuse . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.4 Strategies for Mitigating Security Attacks in Smart Home Systems . . . . . . . . . . 27 2.4.1 Fighting against phishing . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.4.2 Malicious code detection . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.4.3 Tamper resistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.4.4 Security against eavesdropping . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.4.5 Snifng detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.4.6 Network monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.4.7 Secure key management . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.4.8 Physical protection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.5 Strong Security Protocols and Best Practices for Smart Home systems . . . . . . . . 29 2.5.1 Network Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.5.2 Strong Authentication Methods . . . . . . . . . . . . . . . . . . . . . . . . 29 2.5.3 Encryption Protocols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.5.4 Regular Security Audits . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.5.5 Device Firmware Integrity Verification . . . . . . . . . . . . . . . . . . . . 30 2.5.6 Intrusion Detection Systems (IDS) . . . . . . . . . . . . . . . . . . . . . . . 30 2.5.7 Blockchain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.6 Standards and Regulations for smarthome . . . . . . . . . . . . . . . . . . . . . . . 31 .......... Côte titre : MAI/0852

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
MAI/0852	MAI/0852	Mémoire	Bibliothèque des sciences	Anglais	Disponible Disponible

Federated Learning-Based Anomaly Detection Framework for Enhancing Security in Smart Grid Environments / Chaima Talhi

Public ISBD Titre : Federated Learning-Based Anomaly Detection Framework for Enhancing Security in Smart Grid Environments Type de document : document électronique Auteurs : Chaima Talhi ; Nesrine Dehli, Auteur ; Goudjil, Lakhdar, Directeur de thèse Editeur : Setif:UFA Année de publication : 2025 Importance : 1 vol (87 f .) Format : 29 cm Langues : Anglais (eng) Catégories : Thèses & Mémoires:Informatique Mots-clés : Smart Grid Anomaly Detection Federated Learning Machine Learning Deep Learning Index. décimale : 004 Informatique Résumé : The growing integration of smart grids into modern energy infrastructures presents both unprecedented opportunities for intelligent power management and serious concerns regarding data privacy and cyber-security. Traditional anomaly detection methods, although effective, often rely on centralized data collection, thereby increasing the risk of exposing sensitive user information. To overcome these limitations, this thesis introduces FED-XID, a novel Federated Learning-based framework for privacy-preserving anomaly detection in smart grid systems. The framework enables decentralized model training using the XGBoost algorithm and incorporates embedded Intrusion Detection Systems (IDS) at the edge level, ensuring localized monitoring while safeguarding user data confidentiality. In addition, advanced deep learning techniques based on Temporal Convolutional Networks (TCN) and Long Short-Term Memory (LSTM) are applied to handle missing or incomplete smart meter data. The proposed model is both robust and efficient, leveraging a hybrid edge–cloud architecture and a lightweight classification core to ensure high performance, low latency, and scalable deployment in real-world smart grid environments. FED-XID achieved an AUC of 93.60 and a training time of only 25.97 seconds, demonstrating strong detection capability and computational efficiency. Note de contenu : Sommaire Abbreviations 1 General Introduction 4 1 Smart Grids: A Technological Shift Toward Sustainable Energy 7 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.2 Conceptual Foundations and Architecture of Smart Grids . . . . . . . . . . 8 1.2.1 Evolution and Definition of Smart Grids . . . . . . . . . . . . . . . 8 1.2.2 Smart Grids vs Legacy Power Systems . . . . . . . . . . . . . . . . 8 1.2.3 Smart Grid Architecture . . . . . . . . . . . . . . . . . . . . . . . . 9 1.2.4 Key Components of Smart Grids : . . . . . . . . . . . . . . . . . . 14 1.3 Key Challenges in Smart Grid Management . . . . . . . . . . . . . . . . . 16 1.3.1 Demand-Response . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 1.3.2 Load balancing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 1.3.3 Fault Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 1.3.4 Cyber-security Threats: . . . . . . . . . . . . . . . . . . . . . . . . 18 1.4 Role of Edge Computing in Smart Grids . . . . . . . . . . . . . . . . . . . 20 1.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2 Artificial Intelligence Techniques in Smart Grids 23 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.2 Machine Learning (ML) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.2.1 Definition and Core Principles . . . . . . . . . . . . . . . . . . . . . 24 2.2.2 Machine Learning Techniques: . . . . . . . . . . . . . . . . . . . . . 24 2.2.3 Applications of Machine Learning in smart grid . . . . . . . . . . . 26 2.3 Deep Learning (DL) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.3.1 Definition and Core Principles . . . . . . . . . . . . . . . . . . . . . 27 2.3.2 Enhancing Smart Grid Security . . . . . . . . . . . . . . . . . . . . 29 2.4 Federated Learning (FL) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 2.4.1 Overview of Federated Learning (FL) . . . . . . . . . . . . . . . . . 32 2.4.2 Federated Learning vs Traditional Machine Learning . . . . . . . . 32 2.4.3 Key Components of Federated Learning . . . . . . . . . . . . . . . 34 2.4.4 Types of Federated Learning . . . . . . . . . . . . . . . . . . . . . . 36 2.4.5 Applications of Federated Learning in Smart Grids . . . . . . . . . 37 2.4.6 Key FL Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 2.4.7 Benefits of Federated Learning in Smart Grids . . . . . . . . . . . . 39 2.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3 Literature Review: Recent research in Federated Learning-Based Anomaly Detection 42 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 3.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 3.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 3.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4 The Proposed FED-XID Approach 50 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 4.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.3 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.4 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.5 Overview of the Proposed FED-XID Approach . . . . . . . . . . . . . . . 52 4.5.1 Step-by-Step Workflow of the FED-XID . . . . . . . . . . . . . . . 54 4.6 Predictive Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.6.1 XGBoost-Based Local Anomaly Detection on Edge Devices . . . . . 57 4.7 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 4.7.1 Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 4.7.2 Data Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.7.3 Data Pre-processing . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.7.4 Assessing Model Performance . . . . . . . . . . . . . . . . . . . . . 65 4.8 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 4.8.1 Centralized Versus Decentralized XGBoost Classifier . . . . . . . . 70 4.8.2 Comparison with Other State-of-the-Art . . . . . . . . . . . . . . . 70 4.8.3 Comparison with Centralized FVC model . . . . . . . . . . . . . . 71 4.8.4 Discussion and Challenges Encountered . . . . . . . . . . . . . . . . 71 4.9 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 General Conclusion and Future Work 74 A Implementation Stack : Language, Libraries, and FL Tool 84 Côte titre : MAI/1010 Federated Learning-Based Anomaly Detection Framework for Enhancing Security in Smart Grid Environments [document électronique] / Chaima Talhi ; Nesrine Dehli, Auteur ; Goudjil, Lakhdar, Directeur de thèse . - [S.l.] : Setif:UFA, 2025 . - 1 vol (87 f .) ; 29 cm. Langues : Anglais (eng) Catégories : Thèses & Mémoires:Informatique Mots-clés : Smart Grid Anomaly Detection Federated Learning Machine Learning Deep Learning Index. décimale : 004 Informatique Résumé : The growing integration of smart grids into modern energy infrastructures presents both unprecedented opportunities for intelligent power management and serious concerns regarding data privacy and cyber-security. Traditional anomaly detection methods, although effective, often rely on centralized data collection, thereby increasing the risk of exposing sensitive user information. To overcome these limitations, this thesis introduces FED-XID, a novel Federated Learning-based framework for privacy-preserving anomaly detection in smart grid systems. The framework enables decentralized model training using the XGBoost algorithm and incorporates embedded Intrusion Detection Systems (IDS) at the edge level, ensuring localized monitoring while safeguarding user data confidentiality. In addition, advanced deep learning techniques based on Temporal Convolutional Networks (TCN) and Long Short-Term Memory (LSTM) are applied to handle missing or incomplete smart meter data. The proposed model is both robust and efficient, leveraging a hybrid edge–cloud architecture and a lightweight classification core to ensure high performance, low latency, and scalable deployment in real-world smart grid environments. FED-XID achieved an AUC of 93.60 and a training time of only 25.97 seconds, demonstrating strong detection capability and computational efficiency. Note de contenu : Sommaire Abbreviations 1 General Introduction 4 1 Smart Grids: A Technological Shift Toward Sustainable Energy 7 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.2 Conceptual Foundations and Architecture of Smart Grids . . . . . . . . . . 8 1.2.1 Evolution and Definition of Smart Grids . . . . . . . . . . . . . . . 8 1.2.2 Smart Grids vs Legacy Power Systems . . . . . . . . . . . . . . . . 8 1.2.3 Smart Grid Architecture . . . . . . . . . . . . . . . . . . . . . . . . 9 1.2.4 Key Components of Smart Grids : . . . . . . . . . . . . . . . . . . 14 1.3 Key Challenges in Smart Grid Management . . . . . . . . . . . . . . . . . 16 1.3.1 Demand-Response . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 1.3.2 Load balancing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 1.3.3 Fault Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 1.3.4 Cyber-security Threats: . . . . . . . . . . . . . . . . . . . . . . . . 18 1.4 Role of Edge Computing in Smart Grids . . . . . . . . . . . . . . . . . . . 20 1.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2 Artificial Intelligence Techniques in Smart Grids 23 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.2 Machine Learning (ML) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.2.1 Definition and Core Principles . . . . . . . . . . . . . . . . . . . . . 24 2.2.2 Machine Learning Techniques: . . . . . . . . . . . . . . . . . . . . . 24 2.2.3 Applications of Machine Learning in smart grid . . . . . . . . . . . 26 2.3 Deep Learning (DL) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.3.1 Definition and Core Principles . . . . . . . . . . . . . . . . . . . . . 27 2.3.2 Enhancing Smart Grid Security . . . . . . . . . . . . . . . . . . . . 29 2.4 Federated Learning (FL) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 2.4.1 Overview of Federated Learning (FL) . . . . . . . . . . . . . . . . . 32 2.4.2 Federated Learning vs Traditional Machine Learning . . . . . . . . 32 2.4.3 Key Components of Federated Learning . . . . . . . . . . . . . . . 34 2.4.4 Types of Federated Learning . . . . . . . . . . . . . . . . . . . . . . 36 2.4.5 Applications of Federated Learning in Smart Grids . . . . . . . . . 37 2.4.6 Key FL Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 2.4.7 Benefits of Federated Learning in Smart Grids . . . . . . . . . . . . 39 2.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3 Literature Review: Recent research in Federated Learning-Based Anomaly Detection 42 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 3.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 3.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 3.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4 The Proposed FED-XID Approach 50 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 4.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.3 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.4 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.5 Overview of the Proposed FED-XID Approach . . . . . . . . . . . . . . . 52 4.5.1 Step-by-Step Workflow of the FED-XID . . . . . . . . . . . . . . . 54 4.6 Predictive Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.6.1 XGBoost-Based Local Anomaly Detection on Edge Devices . . . . . 57 4.7 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 4.7.1 Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 4.7.2 Data Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.7.3 Data Pre-processing . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.7.4 Assessing Model Performance . . . . . . . . . . . . . . . . . . . . . 65 4.8 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 4.8.1 Centralized Versus Decentralized XGBoost Classifier . . . . . . . . 70 4.8.2 Comparison with Other State-of-the-Art . . . . . . . . . . . . . . . 70 4.8.3 Comparison with Centralized FVC model . . . . . . . . . . . . . . 71 4.8.4 Discussion and Challenges Encountered . . . . . . . . . . . . . . . . 71 4.9 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 General Conclusion and Future Work 74 A Implementation Stack : Language, Libraries, and FL Tool 84 Côte titre : MAI/1010

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
MAI/1010	MAI/1010	Mémoire	Bibliothèque des sciences	Anglais	Disponible Disponible

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