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Produktbild: Cybersecurity in Intelligent Networking Systems

Cybersecurity in Intelligent Networking Systems

153,99 €

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

07.11.2022

Verlag

John Wiley & Sons

Seitenzahl

144

Maße (L/B/H)

22,9/15,2/1 cm

Gewicht

340 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-119-78391-6

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

07.11.2022

Verlag

John Wiley & Sons

Seitenzahl

144

Maße (L/B/H)

22,9/15,2/1 cm

Gewicht

340 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-119-78391-6

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  • Produktbild: Cybersecurity in Intelligent Networking Systems

  • Contents

    Preface xiii

    Acknowledgments xvii

    Acronyms xix

    1 Cybersecurity in the Era of Artificial Intelligence 1

    1.1 Artificial Intelligence for Cybersecurity . 2

    1.1.1 Artificial Intelligence 2

    1.1.2 Machine Learning 4

    1.1.3 Data-Driven Workflow for Cybersecurity . 6

    1.2 Key Areas and Challenges 7

    1.2.1 Anomaly Detection . 8

    1.2.2 Trustworthy Artificial Intelligence . 10

    1.2.3 Privacy Preservation . 10

    1.3 Toolbox to Build Secure and Intelligent Systems . 11

    1.3.1 Machine Learning and Deep Learning . 12

    1.3.2 Privacy-Preserving Machine Learning . 14

    1.3.3 Adversarial Machine Learning . 15

    1.4 Data Repositories for Cybersecurity Research . 16

    1.4.1 NSL-KDD . 17

    1.4.2 UNSW-NB15 . 17

    v

    1.4.3 EMBER 18

    1.5 Summary 18

    2 Cyber Threats and Gateway Defense 19

    2.1 Cyber Threats . 19

    2.1.1 Cyber Intrusions . 20

    2.1.2 Distributed Denial of Services Attack . 22

    2.1.3 Malware and Shellcode . 23

    2.2 Gateway Defense Approaches 23

    2.2.1 Network Access Control 24

    2.2.2 Anomaly Isolation 24

    2.2.3 Collaborative Learning . 24

    2.2.4 Secure Local Data Learning 25

    2.3 Emerging Data-Driven Methods for Gateway Defense 26

    2.3.1 Semi-Supervised Learning for Intrusion Detection 26

    2.3.2 Transfer Learning for Intrusion Detection 27

    2.3.3 Federated Learning for Privacy Preservation . 28

    2.3.4 Reinforcement Learning for Penetration Test 29

    2.4 Case Study: Reinforcement Learning for Automated Post-Breach

    Penetration Test . 30

    2.4.1 Literature Review 30

    2.4.2 Research Idea 31

    2.4.3 Training Agent using Deep Q-Learning 32

    2.5 Summary 34

    vi

    3 Edge Computing and Secure Edge Intelligence 35

    3.1 Edge Computing . 35

    3.2 Key Advances in Edge Computing . 38

    3.2.1 Security 38

    3.2.2 Reliability . 41

    3.2.3 Survivability . 42

    3.3 Secure Edge Intelligence . 43

    3.3.1 Background and Motivation 44

    3.3.2 Design of Detection Module 45

    3.3.3 Challenges against Poisoning Attacks . 48

    3.4 Summary 49

    4 Edge Intelligence for Intrusion Detection 51

    4.1 Edge Cyberinfrastructure . 51

    4.2 Edge AI Engine 53

    4.2.1 Feature Engineering . 53

    4.2.2 Model Learning . 54

    4.2.3 Model Update 56

    4.2.4 Predictive Analytics . 56

    4.3 Threat Intelligence 57

    4.4 Preliminary Study . 57

    4.4.1 Dataset 57

    4.4.2 Environment Setup . 59

    4.4.3 Performance Evaluation . 59

    vii

    4.5 Summary 63

    5 Robust Intrusion Detection 65

    5.1 Preliminaries 65

    5.1.1 Median Absolute Deviation . 65

    5.1.2 Mahalanobis Distance 66

    5.2 Robust Intrusion Detection . 67

    5.2.1 Problem Formulation 67

    5.2.2 Step 1: Robust Data Preprocessing 68

    5.2.3 Step 2: Bagging for Labeled Anomalies 69

    5.2.4 Step 3: One-Class SVM for Unlabeled Samples . 70

    5.2.5 Step 4: Final Classifier . 74

    5.3 Experiment and Evaluation . 76

    5.3.1 Experiment Setup 76

    5.3.2 Performance Evaluation . 81

    5.4 Summary 92

    6 Efficient Preprocessing Scheme for Anomaly Detection 93

    6.1 Efficient Anomaly Detection . 93

    6.1.1 Related Work . 95

    6.1.2 Principal Component Analysis . 97

    6.2 Efficient Preprocessing Scheme for Anomaly Detection . 98

    6.2.1 Robust Preprocessing Scheme . 99

    6.2.2 Real-Time Processing 103

    viii

    6.2.3 Discussions 103

    6.3 Case Study . 104

    6.3.1 Description of the Raw Data 105

    6.3.2 Experiment 106

    6.3.3 Results 108

    6.4 Summary 109

    7 Privacy Preservation in the Era of Big Data 111

    7.1 Privacy Preservation Approaches 111

    7.1.1 Anonymization 111

    7.1.2 Differential Privacy . 112

    7.1.3 Federated Learning . 114

    7.1.4 Homomorphic Encryption 116

    7.1.5 Secure Multi-Party Computation . 117

    7.1.6 Discussions 118

    7.2 Privacy-Preserving Anomaly Detection . 120

    7.2.1 Literature Review 121

    7.2.2 Preliminaries . 123

    7.2.3 System Model and Security Model 124

    7.3 Objectives and Workflow . 126

    7.3.1 Objectives . 126

    7.3.2 Workflow . 128

    7.4 Predicate Encryption based Anomaly Detection . 129

    7.4.1 Procedures 129

    ix

    7.4.2 Development of Predicate . 131

    7.4.3 Deployment of Anomaly Detection 132

    7.5 Case Study and Evaluation . 134

    7.5.1 Overhead . 134

    7.5.2 Detection . 136

    7.6 Summary 137

    8 Adversarial Examples: Challenges and Solutions 139

    8.1 Adversarial Examples . 139

    8.1.1 Problem Formulation in Machine Learning 140

    8.1.2 Creation of Adversarial Examples . 141

    8.1.3 Targeted and Non-Targeted Attacks . 141

    8.1.4 Black-Box and White-Box Attacks 142

    8.1.5 Defenses against Adversarial Examples 142

    8.2 Adversarial Attacks in Security Applications 143

    8.2.1 Malware 143

    8.2.2 Cyber Intrusions . 143

    8.3 Case Study: Improving Adversarial Attacks Against Malware

    Detectors 144

    8.3.1 Background 144

    8.3.2 Adversarial Attacks on Malware Detectors 145

    8.3.3 MalConv Architecture 147

    8.3.4 Research Idea 148

    8.4 Case Study: A Metric for Machine Learning Vulnerability to

    Adversarial Examples . 149

    8.4.1 Background 149

    8.4.2 Research Idea 150

    8.5 Case Study: Protecting Smart Speakers from Adversarial Voice

    Commands . 153

    8.5.1 Background 153

    8.5.2 Challenges 154

    8.5.3 Directions and Tasks 155

    8.6 Summary 157

    xi