CYBERSECURITY IN INTELLIGENT NETWORKING SYSTEMS Help protect your network system with this important reference work on cybersecurity Cybersecurity and privacy are critical to modern network systems. As various malicious threats 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 protecting the security of modern network systems and ensuring information privacy. Cybersecurity in Intelligent Networking Systems provides a background introduction to data-driven cybersecurity, privacy preservation, and adversarial machine learning. It offers 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 robust and trustworthy safeguards with edge-enabled cyber infrastructure, edge-enabled artificial intelligence (AI) engines, and threat intelligence. Focusing on encryption-based security protocol, this book also 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. Cybersecurity in Intelligent Networking Systems readers will also find: Fundamentals in AI for cybersecurity, including artificial intelligence, machine learning, and security threats Latest technologies in data-driven privacy preservation, including differential privacy, federated learning, and homomorphic encryption Key areas in adversarial machine learning, from both offense and defense perspectives Descriptions of network anomalies and cyber threats Background information on data-driven network intelligence for cybersecurity Robust and secure edge intelligence for network anomaly detection against cyber intrusions Detailed descriptions of the design of privacy-preserving security protocols Cybersecurity in Intelligent Networking Systems is an essential reference for all professional computer engineers and researchers in cybersecurity and artificial intelligence, as well as graduate students in these fields.

Table of Contents:
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