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

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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

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

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