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AI for cybersecurity : research and practice /

Informative reference on the state of the art in cybersecurity and how to achieve a more secure cyberspace AI for Cybersecurity presents the state of the art and practice in AI for cybersecurity with a focus on four interrelated defensive capabilities of deter, protect, detect, and respond. The book...

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Bibliographic Details
Other Authors: Song, Houbing Herbert, Bertino, Elisa, Vasquez, Alvaro, Wang, Huihui Helen, Shoshitaishvili, Yan, Jha, Sumit Kumar
Format: eBook
Language:English
Published: [S.l.] : John Wiley and Sons, Inc.; Wiley-IEEE Press, [n.d.]
Edition:1.
Subjects:
Computer security.
Artificial intelligence > Security measures.
Computer networks > Security measures.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • List of Contributors xix Foreword xxvii About the Editors xxxi Preface xxxv Acknowledgments xxxvii
  • 1 LLMs Are Not Few-shot Threat Hunters 1 Glenn A. Fink, Luiz M. Pereira, and Christian W. Stauffer
  • 1.1 Overview 1 1.2 Large Language Models 4 1.3 Threat Hunters 12 1.4 Capabilities and Limitations of LLMs in Cybersecurity 18 1.5 Conclusion: Reimagining LLMs as Assistant Threat Hunter 24
  • 2 LLMs on Support of Privacy and Security of Mobile Apps: State-of-the-art and Research Directions 29 Tran Thanh Lam Nguyen, Barbara Carminati, and Elena Ferrari
  • 2.1 Introduction 29 2.2 Background on LLMs 32 2.3 Mobile Apps: Main Security and Privacy Threats 43 2.4 LLM-based Solutions: State-of-the-art 47 2.5 An LLMs-based Approach for Mitigating Image Metadata Leakage Risks 53 2.6 Research Challenges 57 2.7 Conclusion 60
  • 3 Machine Learning-based Intrusion Detection Systems: Capabilities, Methodologies, and Open Research Challenges 67 Chaoyu Zhang, Ning Wang, Y. Thomas Hou, and Wenjing Lou
  • 3.1 Introduction 67 3.2 Basic Concepts and ML for Intrusion Detection 69 3.3 Capability I: Zero-day Attack Detection with ML 75 3.4 Capability II: Intrusion Explainability Through XAI 79 3.5 Capability III: Intrusion Detection in Encrypted Traffic 84 3.6 Capability IV: Context-aware Threat Detection and Reasoning with GNNs 88 3.7 Capability V: LLMs for Intrusion Detection and Understanding 93 3.8 Summary 97
  • 4 Generative AI for Advanced Cyber Defense 109 Moqsadur Rahman, Aaron Sanchez, Krish Piryani, Siddhartha Das, Sai Munikoti, Luis de la Torre Quintana, Monowar Hasan, Joseph Aguayo, Monika Akbar, Shahriar Hossain, and Mahantesh Halappanavar
  • 4.1 Introduction 109 4.2 Motivation and Related Work 111 4.3 Foundations for Cyber Defense 114 4.4 Retrieval-augmented Generation 117 4.5 KG and Querying 118 4.6 Evaluation and Results 126 4.7 Conclusion 142
  • 5 Enhancing Threat Detection and Response with Generative AI and Blockchain 147 Driss El Majdoubi, Souad Sadki, Zakia El Uahhabi, and Mohamed Essaidi
  • 5.1 Introduction 147 5.2 Cybersecurity Current Issues: Background 148 5.3 Blockchain Technology for Cybersecurity 150 5.4 Combining Generative AI and Blockchain for Cybersecurity 156 5.5 Conclusion 162
  • 6 Privacy-preserving Collaborative Machine Learning 169 Runhua Xu and James Joshi
  • 6.1 Introduction 169 6.2 Collaborative Learning Overview 172 6.3 Collaborative Learning Paradigms and Privacy Risks 177 6.4 Privacy-preserving Technologies 187 6.5 Conclusion 195
  • 7 Security and Privacy in Federated Learning 203 Zhuosheng Zhang and Shucheng Yu
  • 7.1 Introduction 203 7.2 Privacy-preserving FL 215 7.3 Enhance Security in FL 219 7.4 Secure Privacy-preserving FL 225 7.5 Conclusion 228
  • 8 Machine Learning Attacks on Signal Characteristics in Wireless Networks 235 Yan Wang, Cong Shi, Yingying Chen, and Zijie Tang
  • 8.1 Introduction 235 8.2 Threat Model and Targeted Models 239 8.3 Attack Formulation and Challenges 241 8.4 Poison-label Backdoor Attack 246 8.5 Clean-label Backdoor Trigger Design 252 8.6 Evaluation 255 8.7 Related Work 261 8.8 Conclusion 262
  • 9 Secure by Design 267 Mehdi Mirakhorli and Kevin E. Greene
  • 9.1 Introduction 267 9.2 A Methodological Approach to Secure by Design 275 9.3 AI in Secure by Design: Opportunities and Challenges 283 9.4 Conclusion and Future Directions 284
  • 10 DDoS Detection in IoT Environments: Deep Packet Inspection and Real-world Applications 289 Nikola Gavric, Guru Bhandari, and Andrii Shalaginov
  • 10.1 Introduction 289 10.2 DDoS Detection Techniques in Research 294 10.3 Limitations of Research Approaches 303 10.4 Industry Practices for DDoS Detection 305 10.5 Challenges in DDoS Detection 309 10.6 Future Directions 311 10.7 Conclusion 313
  • 11 Data Science for Cybersecurity: A Case Study Focused on DDoS Attacks 317 Michele Nogueira, Ligia F. Borges, and Anderson B. Neira
  • 11.1 Introduction 317 11.2 Background 319 11.3 State of the Art 333 11.4 Challenges and Opportunities 340 11.5 Conclusion 341
  • 12 AI Implications for Cybersecurity Education and Future Explorations 347 Elizabeth Hawthorne, Mihaela Sabin, and Melissa Dark
  • 12.1 Introduction 347 12.2 Postsecondary Cybersecurity Education: Historical Perspective and Current Initiatives 348 12.3 Cybersecurity Policy in Secondary Education 361 12.4 Conclusion 367 12.5 Future Explorations 368
  • 13 Ethical AI in Cybersecurity: Quantum-resistant Architectures and Decentralized Optimization Strategies 371 Andreou Andreas, Mavromoustakis X. Constandinos, Houbing Song, and Jordi Mongay Batalla
  • 13.1 Introduction 371 13.2 Literature Review 373 13.3 Overview and Ethical Considerations in AI-centric Cybersecurity 374 13.4 AML and Privacy Risks in AI Systems 378 13.5 Forensic and Formal Methods for AI Security 380 13.6 Generative AI and Quantum-resistant Architectures in Cybersecurity 385 13.7 Future Directions and Ethical Considerations 387 13.8 Conclusion 390
  • 14 Security Threats and Defenses in AI-enabled Object Tracking Systems 397 Mengjie Jia, Yanyan Li, and Jiawei Yuan
  • 14.1 Introduction 397 14.2 Related Works 398 14.3 Methods 401 14.4 Evaluation 405 14.5 Conclusion 413
  • 15 AI for Android Malware Detection and Classification 419 Safayat Bin Hakim, Muhammad Adil, Kamal Acharya, and Houbing Herbert Song
  • 15.1 Introduction 419 15.2 Design of the Proposed Framework 424 15.3 Implementation and Dataset Overview 428 15.4 Results and Insights 431 15.5 Feature Importance Analysis 439 15.6 Comparative Analysis and Advancements over Existing Methods 442 15.7 Discussion 446 15.8 Conclusion 447
  • 16 Cyber-AI Supply Chain Vulnerabilities 451 Joanna C. S. Santos
  • 16.1 Introduction 451 16.2 AI/ML Supply Chain Attacks via Untrusted Model Deserialization 452 16.3 The State-of-the-art of the AI/ML Supply Chain 458 16.4 Conclusion 466
  • 17 AI-powered Physical Layer Security in Industrial Wireless Networks 471 Hong Wen, Qi Wang, and Zhibo Pang
  • 17.1 Introduction 471 17.2 Radio Frequency Fingerprint Identification 474 17.3 CSI-based PLA 481 17.4 PLK Distribution 493 17.5 Physical Layer Security Enhanced ZT Security Framework 498
  • 18 The Security of Reinforcement Learning Systems in Electric Grid Domain 505 Suman Rath, Zain ul Abdeen, Olivera Kotevska, Viktor Reshniak, and Vivek Kumar Singh
  • 18.1 Introduction 505 18.2 RL for Control 506 18.3 Case Study: RL for Control in Cyber-physical Microgrids 513 18.4 Related Work: Grid Applications of RL 516 18.5 Open Challenges and Solutions 518 18.6 Conclusion 522
  • 19 Geopolitical Dimensions of AI in Cybersecurity: The Emerging Battleground 533 Felix Staicu and Mihai Barloiu
  • 19.1 Introduction 533 19.2 Foundations of AI in Geopolitics: From Military Origins to Emerging Strategic Trajectories 536 19.3 The Contemporary Battleground: AI as a Strategic Variable 540 19.4 Beyond Today's Conflicts: Future Horizons in AI-driven Security 548 19.5 Conclusions and Recommendations 558 19.6 Conclusion 560
  • 20 Robust AI Techniques to Support High-consequence Applications in the Cyber Age 567 Joel Brogan, Linsey Passarella, Mark Adam, Birdy Phathanapirom, Nathan Martindale, Jordan Stomps, Olivera Kotevska, Matthew Yohe, Ryan Tokola, Ryan Kerekes, and Scott Stewart
  • 20.1 Introduction 567 20.2 Motivation 568 20.3 Explainability Measures for Deep Learning in High-consequence Scenarios 570 20.4 Improving Confidence and Robustness Measures for Deep Learning in Critical Decision-making Scenarios 573 20.5 Building Robust AI Through SME Knowledge Embeddings 583 20.6 Flight-path Vocabularies for Foundation Model Training 588 20.7 Promise and Peril of Foundation Models in High-consequence Scenarios 592 20.8 Discussion 596
  • Acknowledgments 596 References 596 Index 601.