Cybersecurity in Intelligent Networking Systems
1st Edition
Published on 15. December 2022
Book
Hardback
160 pages
978-1-119-78391-6 (ISBN)
Description
Help protect your network with this important reference work on cyber security Cyber threats are a common issue to your security and privacy. As various malicious threats and malware have been launched that target critical online services-such as e-commerce, e-health, social networks, and other major cyber applications-it has become more critical to protect important information from being accessed. Data-driven network intelligence is a crucial development in preserving the integrity of your computer by identifying anomalies and ensuring information privacy.
Data-driven Network Intelligence for Cyber Security provides a comprehensive introduction to exploring technologies, applications, and issues in data-driven cyber infrastructure. It describes a proposed novel, data-driven network intelligence system that helps provide safeguards for edge-enabled infrastructure, edge-enabled artificial intelligence (AI) engines, and threat intelligence. Focusing particularly on encryption-based anomaly detection protocol, this book highlights the capability of a network intelligence system in helping target and identify unauthorized access, malicious interactions, and the destruction of critical information and communication technology.
Data-driven Network Intelligence readers will also find:
Discussion of network anomalies and cyber threats
Background information on AI and cyber infrastructure, particularly as regards trustworthy and robust artificial intelligence applications
A presentation of a system and security model in the edge intelligence
Detailed descriptions of the design of predicates and privacy-preserving anomaly detection features
Data-driven Network Intelligence for Cyber Security is an essential reference for all professional computer engineers and researchers in cyber security and artificial intelligence, as well as graduate students in these fields.
Data-driven Network Intelligence for Cyber Security provides a comprehensive introduction to exploring technologies, applications, and issues in data-driven cyber infrastructure. It describes a proposed novel, data-driven network intelligence system that helps provide safeguards for edge-enabled infrastructure, edge-enabled artificial intelligence (AI) engines, and threat intelligence. Focusing particularly on encryption-based anomaly detection protocol, this book highlights the capability of a network intelligence system in helping target and identify unauthorized access, malicious interactions, and the destruction of critical information and communication technology.
Data-driven Network Intelligence readers will also find:
Discussion of network anomalies and cyber threats
Background information on AI and cyber infrastructure, particularly as regards trustworthy and robust artificial intelligence applications
A presentation of a system and security model in the edge intelligence
Detailed descriptions of the design of predicates and privacy-preserving anomaly detection features
Data-driven Network Intelligence for Cyber Security is an essential reference for all professional computer engineers and researchers in cyber security and artificial intelligence, as well as graduate students in these fields.
More details
Language
English
Place of publication
Hoboken
United States
Publishing group
John Wiley and Sons Ltd
Target group
Professional and scholarly
Dimensions
Height: 229 mm
Width: 152 mm
Thickness: 10 mm
Weight
367 gr
ISBN-13
978-1-119-78391-6 (9781119783916)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Shengjie Xu | Yi Qian | Rose Qingyang Hu
Cybersecurity in Intelligent Networking Systems
E-Book
11/2022
1st Edition
Wiley
€110.99
Available for download
Shengjie Xu | Yi Qian | Rose Qingyang Hu
Cybersecurity in Intelligent Networking Systems
E-Book
11/2022
1st Edition
Wiley
€110.99
Available for download
Person
Shengjie Xu, PhD, is Assistant Professor of the Beacom College of Computer and Cyber Sciences Department at Dakota State University, US.
Yi Qian, PhD, is an IEEE member and is a Professor in the Department of Electrical and Computer Engineering at the University of Nebraska-Lincoln, US
Rose Qingyang Hu, PhD, is an IEEE member. She is also a Professor with the Electrical and Computer Engineering Department and the Associate Dean for Research of the College of Engineering, Utah State University, US.
Yi Qian, PhD, is an IEEE member and is a Professor in the Department of Electrical and Computer Engineering at the University of Nebraska-Lincoln, US
Rose Qingyang Hu, PhD, is an IEEE member. She is also a Professor with the Electrical and Computer Engineering Department and the Associate Dean for Research of the College of Engineering, Utah State University, US.
Content
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
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