Cybersecurity in Intelligent Networking Systems

Shengjie Xu , Yi Qian , Rose Qingyang Hu 지음

Wiley-IEEE Press 2022년 11월 02일 출간

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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 inte

목차

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

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저자(글) Shengjie Xu

저자(글) Yi Qian

저자(글) Rose Qingyang Hu