University Sétif 1 FERHAT ABBAS Faculty of Sciences
Détail de l'auteur
Auteur Goudjil, Lakhdar |
Documents disponibles écrits par cet auteur
Ajouter le résultat dans votre panier Affiner la rechercheBlockchain-based deep learning to improve the security of the Industrial Internet of Things / Aya Bendjedi
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
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 4.0Index. 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 : 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 4.0Index. 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 : Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité MAI/0714 MAI/0714 Mémoire Bibliothèque des sciences Anglais Disponible
Disponible
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 SecurityIndex. 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 SecurityIndex. 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
DisponibleFederated Learning-Based Anomaly Detection Framework for Enhancing Security in Smart Grid Environments / Chaima Talhi
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 LearningIndex. 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 84Côte 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 LearningIndex. 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 84Côte titre : MAI/1010 Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité MAI/1010 MAI/1010 Mémoire Bibliothèque des sciences Anglais Disponible
Disponible
Titre : "PHARMACEUTICAL SUPPLY CHAIN MANAGEMENT" Type de document : document électronique Auteurs : Ishak Kerrouche ; Goudjil, Lakhdar, Directeur de thèse Editeur : Setif:UFA Année de publication : 2024 Importance : 1 vol (66 f .) Format : 29 cm Langues : Anglais (eng) Catégories : Thèses & Mémoires:Informatique Mots-clés : Inventory Management
Demand Forecasting
Counterfeit MedicinesIndex. décimale : 004 Informatique Résumé :
Effective management of inventory, demand, transportation, and quality is essential for achieving operational efficiency and maintaining product integrity within the pharmaceutical sector. This document examines fundamental principles such as cost reduction, demand forecasting, cold chain logistics, and quality control aimed at enhancing supply chain performance. A significant focus is placed on addressing the issue of counterfeit medications through the implementation of Good Supply Chain Practices (GSCP) and advanced traceability technologies. For platforms like “PharmaConnect,” these strategies are critical for fostering collaboration among pharmacies, manufacturers, and distributors, ensuring the prompt delivery of genuine medicines, and upholding regulatory standards. By utilizing innovative solutions and strong management practices, “PharmaConnect” contributes to public health and improves industry efficiency.Note de contenu :
Sommaire
GENERAL INTRODUCTION………………………………………………………..…1
Theoretical part
Chapter I. Stock management
I.1. Introduction……………………………………………………………………...……3
I.2. Definition of stock management………………………………….……...…...4
I.3. Principles of stock management………………………………………………..…5
I.4. Stock management methods………………………………………………………………5
I.4.1. FIFO method (First in, First Out)……………….…………………………………6
I.4.2. LIFO Method (Last in, First Out)………………………………………………….6
I.4.3. Weighted average cost method………………………………..…………………...6
I.4.4. ABC analysis………………………………………………………………….…...7
I.4.5. Computerized inventory management systems……………….……………….…..7
I.5. Optimization of stock levels…………………………………………..……………….…..9
I.6. Management of pharmaceutical products………….………………...……….…11
I.6.1. Temperature control………………………………….…...………………………11
I.6.2. Management of expiration dates……………………………………………….…11
I.6.3. Regulatory conformity…………………………………….…...………………....11
I.6.4. Product traceability………………………………………….……………………11
I.6.5. Orders and supplies management………………………….……………………..11
I.6.6. Security and confidentiality…………………………………..…………………..12
I.7. Conclusion ……………………………………………………………………………..….13
Chapter II. Demand planning
II.1.Introduction………………………………………………………………….…………...14
II.2. Definition of demand planning………………..………………………..……….15
II.3. Forecasting of the demand for drugs……………………..………….…….……16
II.34 Factors influencing the demand for drugs…………………….……….……….17
II.4.1. Demography……………………………………………………………………..17
II.4.2. Epidemiology and Public Health……………………………….………………..18
II.4.3. Medical and technological advances……………………..……….…….……….18
II.4.4. Health policies and regulations .…………………………………...……………18
II.4.5. Consumer behavior and health professionals……………………….………….18
II.5. demand planning methods………………………………………….….………19
II.5.1. Quantitative forecasts……………………..…………...……………………….19
II.5.2. Qualitative Forecasting…………………….……………………….…………. 21
II.6. Demand Management Strategies……………………………..…………….………….22
II.7. Conclusion…………………………………………………………..……….…………..24
Chapter III. Transport and Logistics
III.1. Introduction…………………………………………………………….…………..…..25
III.2. Definition of Transportation and Logistics………………………………...….26
III.3. Transportation of Pharmaceuticals……………………………….........……..26
III.4. Cold Chain Requirements…………………………...…………………………………28
III.5. Pharmaceutical Logistics……………………………………………...………..29
III.6. Safety Stock Management………………………………………………………30
III.7. Conclusion…………………………………………………………………..…………………32
Chapter IV. Quality Management
IV.1. Introduction…………………………………………………………..………………....33
IV.2. Definition of Quality Management …………………………………………… 34
IV.3. Good Manufacturing Practices (GMP) …………………..………..…………35
IV.4. Good Distribution Practices (GDP)…………………………………….………36
IV.5. Quality Management System (QMS)…………………..………….…………..38
IV.6. Quality control of medicinal products…………………………………...……..41
IV.7. Conclusion…………………………………………………………………..…….……..43
Chapter V. Fight against counterfeiting
V.1. Introduction…………………………………………………….……….………………..44
V.2. Definition of anti-counterfeiting……………………………………….………...45
V.3. Anti-counterfeiting efforts…………………………………………..…..……….45
V.4. Scope of the problem of counterfeit medicines………..………..………………46
V.5. Risks Associated with Counterfeit Medicines……………...….………………..47
V.6. Anti-counterfeiting strategies……………………………………………………48
V.7. Role of GSCP in the fight against counterfeiting…………….……..………….50
V.8. Conclusion………………………………………………………………………...……...52
Chapter VI. PRACTICAL PART
PharmaConnect Technical Specifications…………………………..……………………….53
VI.1. Overview………………………………………………………………………...53
VI.2.1. Technologies…………………………………………………………………...53
VI.2.2. Features……………………………………………………………...…………53
VI.3. Frontend…………………………………………………………………………54
VI.3.1. Technologies………………………………………………..………….………54
VI.3.2. Features……………………………………………………...…………………54
VI.4. Security…………………………………………………………………………..54
VI.4.1. Authentication and Authorization………………………...……………………54
VI.5. Documentation…….......………………………………………………………..55
VI.5.1. API Documentation………………..........................………………………………..55
the start-up projects
FIRST AXIS Presentation of the start-up project
I. Presentation of the project………………………………………………….………………56
I.1. The project idea (proposed solution)…………………………………….…………56
I.2. The proposed values…………………………………………………………...……56
I.3. Teamwork ……………………………………...…..…………………………….....57
I.4. The project’s objectives………………………………..……………..……………..57
I.5. Completion schedule…………………………………….……………..……………58
SECOND AXIS Innovative aspects
II. Innovative aspects………………………………………….…………………………….59
II.1. The nature of the innovation……….…………………..……………………59
II.2. The field of innovation…………………..…………………..………………..59
THIRD AXIS Strategic market analysis
III. Strategic market analysis…………………………….…...……………………………60
III.1. The market segments………………………….………………..…….……..60
III.2. The intensity of competition for this project..……………………………..60
III.3. Project marketing strategy………………………….………………………61
FOURTH AXIS Production and Organization plan
IV. Production and organization plan………………….……………………..……………….62
IV.1. Procurement……………………………………….………………..…………………62
IV.2. Workforce (employees)………………………………..………….…………………..62
IV.3. The Main Partners……………………………………..………..…..………………62
FIFTH AXIS Financial plan
V. Financial Plan………………………………………………….……..…………………..64
V.1. Costs and expenses………………………………………………..……………………64Côte titre : MAI/0960 "PHARMACEUTICAL SUPPLY CHAIN MANAGEMENT" [document électronique] / Ishak Kerrouche ; Goudjil, Lakhdar, Directeur de thèse . - [S.l.] : Setif:UFA, 2024 . - 1 vol (66 f .) ; 29 cm.
Langues : Anglais (eng)
Catégories : Thèses & Mémoires:Informatique Mots-clés : Inventory Management
Demand Forecasting
Counterfeit MedicinesIndex. décimale : 004 Informatique Résumé :
Effective management of inventory, demand, transportation, and quality is essential for achieving operational efficiency and maintaining product integrity within the pharmaceutical sector. This document examines fundamental principles such as cost reduction, demand forecasting, cold chain logistics, and quality control aimed at enhancing supply chain performance. A significant focus is placed on addressing the issue of counterfeit medications through the implementation of Good Supply Chain Practices (GSCP) and advanced traceability technologies. For platforms like “PharmaConnect,” these strategies are critical for fostering collaboration among pharmacies, manufacturers, and distributors, ensuring the prompt delivery of genuine medicines, and upholding regulatory standards. By utilizing innovative solutions and strong management practices, “PharmaConnect” contributes to public health and improves industry efficiency.Note de contenu :
Sommaire
GENERAL INTRODUCTION………………………………………………………..…1
Theoretical part
Chapter I. Stock management
I.1. Introduction……………………………………………………………………...……3
I.2. Definition of stock management………………………………….……...…...4
I.3. Principles of stock management………………………………………………..…5
I.4. Stock management methods………………………………………………………………5
I.4.1. FIFO method (First in, First Out)……………….…………………………………6
I.4.2. LIFO Method (Last in, First Out)………………………………………………….6
I.4.3. Weighted average cost method………………………………..…………………...6
I.4.4. ABC analysis………………………………………………………………….…...7
I.4.5. Computerized inventory management systems……………….……………….…..7
I.5. Optimization of stock levels…………………………………………..……………….…..9
I.6. Management of pharmaceutical products………….………………...……….…11
I.6.1. Temperature control………………………………….…...………………………11
I.6.2. Management of expiration dates……………………………………………….…11
I.6.3. Regulatory conformity…………………………………….…...………………....11
I.6.4. Product traceability………………………………………….……………………11
I.6.5. Orders and supplies management………………………….……………………..11
I.6.6. Security and confidentiality…………………………………..…………………..12
I.7. Conclusion ……………………………………………………………………………..….13
Chapter II. Demand planning
II.1.Introduction………………………………………………………………….…………...14
II.2. Definition of demand planning………………..………………………..……….15
II.3. Forecasting of the demand for drugs……………………..………….…….……16
II.34 Factors influencing the demand for drugs…………………….……….……….17
II.4.1. Demography……………………………………………………………………..17
II.4.2. Epidemiology and Public Health……………………………….………………..18
II.4.3. Medical and technological advances……………………..……….…….……….18
II.4.4. Health policies and regulations .…………………………………...……………18
II.4.5. Consumer behavior and health professionals……………………….………….18
II.5. demand planning methods………………………………………….….………19
II.5.1. Quantitative forecasts……………………..…………...……………………….19
II.5.2. Qualitative Forecasting…………………….……………………….…………. 21
II.6. Demand Management Strategies……………………………..…………….………….22
II.7. Conclusion…………………………………………………………..……….…………..24
Chapter III. Transport and Logistics
III.1. Introduction…………………………………………………………….…………..…..25
III.2. Definition of Transportation and Logistics………………………………...….26
III.3. Transportation of Pharmaceuticals……………………………….........……..26
III.4. Cold Chain Requirements…………………………...…………………………………28
III.5. Pharmaceutical Logistics……………………………………………...………..29
III.6. Safety Stock Management………………………………………………………30
III.7. Conclusion…………………………………………………………………..…………………32
Chapter IV. Quality Management
IV.1. Introduction…………………………………………………………..………………....33
IV.2. Definition of Quality Management …………………………………………… 34
IV.3. Good Manufacturing Practices (GMP) …………………..………..…………35
IV.4. Good Distribution Practices (GDP)…………………………………….………36
IV.5. Quality Management System (QMS)…………………..………….…………..38
IV.6. Quality control of medicinal products…………………………………...……..41
IV.7. Conclusion…………………………………………………………………..…….……..43
Chapter V. Fight against counterfeiting
V.1. Introduction…………………………………………………….……….………………..44
V.2. Definition of anti-counterfeiting……………………………………….………...45
V.3. Anti-counterfeiting efforts…………………………………………..…..……….45
V.4. Scope of the problem of counterfeit medicines………..………..………………46
V.5. Risks Associated with Counterfeit Medicines……………...….………………..47
V.6. Anti-counterfeiting strategies……………………………………………………48
V.7. Role of GSCP in the fight against counterfeiting…………….……..………….50
V.8. Conclusion………………………………………………………………………...……...52
Chapter VI. PRACTICAL PART
PharmaConnect Technical Specifications…………………………..……………………….53
VI.1. Overview………………………………………………………………………...53
VI.2.1. Technologies…………………………………………………………………...53
VI.2.2. Features……………………………………………………………...…………53
VI.3. Frontend…………………………………………………………………………54
VI.3.1. Technologies………………………………………………..………….………54
VI.3.2. Features……………………………………………………...…………………54
VI.4. Security…………………………………………………………………………..54
VI.4.1. Authentication and Authorization………………………...……………………54
VI.5. Documentation…….......………………………………………………………..55
VI.5.1. API Documentation………………..........................………………………………..55
the start-up projects
FIRST AXIS Presentation of the start-up project
I. Presentation of the project………………………………………………….………………56
I.1. The project idea (proposed solution)…………………………………….…………56
I.2. The proposed values…………………………………………………………...……56
I.3. Teamwork ……………………………………...…..…………………………….....57
I.4. The project’s objectives………………………………..……………..……………..57
I.5. Completion schedule…………………………………….……………..……………58
SECOND AXIS Innovative aspects
II. Innovative aspects………………………………………….…………………………….59
II.1. The nature of the innovation……….…………………..……………………59
II.2. The field of innovation…………………..…………………..………………..59
THIRD AXIS Strategic market analysis
III. Strategic market analysis…………………………….…...……………………………60
III.1. The market segments………………………….………………..…….……..60
III.2. The intensity of competition for this project..……………………………..60
III.3. Project marketing strategy………………………….………………………61
FOURTH AXIS Production and Organization plan
IV. Production and organization plan………………….……………………..……………….62
IV.1. Procurement……………………………………….………………..…………………62
IV.2. Workforce (employees)………………………………..………….…………………..62
IV.3. The Main Partners……………………………………..………..…..………………62
FIFTH AXIS Financial plan
V. Financial Plan………………………………………………….……..…………………..64
V.1. Costs and expenses………………………………………………..……………………64Côte titre : MAI/0960 Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité MAI/0960 MAI/0960 Mémoire Bibliothèque des sciences Anglais Disponible
DisponiblePermalink
Permalink
Permalink
PermalinkPermalinkPermalink
