AI for Cybersecurity : Research and Practice

Click to enlarge

Author(s):	Song
ISBN No.:	9781394293773
Pages:	512
Year:	202601
Format:	E-Book
Price:	$ 251.21
Dispatch delay:	Dispatched between 7 to 15 days
Status:	Available (Forthcoming)

Qty: Add to Cart

Synopsis
Table of contents
Full Details

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 1.1.1 AI Is Not Magic 1 1.1.

2 Inherent Difficulty of Human Tasks in Cybersecurity and Threat Hunting 3 1.2 Large Language Models 4 1.2.1 Background 4 1.2.2 Transformers 4 1.2.3 Pretraining and Fine-tuning 9 1.

2.4 General Limitations 9 1.3 Threat Hunters 12 1.3.1 Introduction to Threat Hunting 12 1.3.2 The Dimensions of Threat Hunting 13 1.3.

3 The Approaches to Threat Hunting 15 1.3.4 The Process of Threat Hunting 16 1.3.5 Challenges to Modern Threat Hunting 17 1.4 Capabilities and Limitations of LLMs in Cybersecurity 18 1.4.1 General Limitations of LLMs for Cybersecurity 18 1.

4.2 General Capabilities of LLMs Useful for Cybersecurity 20 1.4.3 Applications of LLMs in Cybersecurity 22 1.5 Conclusion: Reimagining LLMs as Assistant Threat Hunter 24 References 27 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 2.2.

1 Large Language Models 32 2.2.2 FSL and RAG 39 2.3 Mobile Apps: Main Security and Privacy Threats 43 2.4 LLM-based Solutions: State-of-the-art 47 2.4.1 Vulnerabilities Detection 48 2.4.

2 Bug Detection and Reproduction 50 2.4.3 Malware Detection 52 2.5 An LLMs-based Approach for Mitigating Image Metadata Leakage Risks 53 2.6 Research Challenges 57 2.7 Conclusion 60 Acknowledgment 61 References 61 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 3.2.1 Fundamental Concepts 69 3.2.2 ml Algorithms for Intrusion Detection 70 3.2.3 Taxonomy of IDSs 72 3.2.

4 Evaluation Metrics and Datasets 73 3.3 Capability I: Zero-day Attack Detection with ml 75 3.3.1 Understanding Zero-day Attacks and Their Impact 75 3.3.2 General Workflow of ML-IDS for Identifying Zero-day Attacks 75 3.3.3 Anomaly Detection Mechanisms 76 3.

3.4 Open Research Challenges 77 3.4 Capability II: Intrusion Explainability Through XAI 79 3.4.1 Enhancing Transparency and Trust in Intrusion Detection 79 3.4.2 General Workflow of XAI 80 3.4.

3 XAI Methods for IDS Transparency Enhancement 80 3.4.4 Open Research Challenges 83 3.5 Capability III: Intrusion Detection in Encrypted Traffic 84 3.5.1 Challenges in Intrusion Detection for Encrypted Traffic 84 3.5.2 Workflow of ML-IDS for Encrypted Traffic 84 3.

5.3 ML-based Solutions for Encrypted Traffic Analysis 84 3.5.4 Open Research Challenges 87 3.6 Capability IV: Context-aware Threat Detection and Reasoning with GNNs 88 3.6.1 Introduction to GNNs in IDS 88 3.6.

2 Workflow of GNNs for Intrusion Detection 88 3.6.3 Provenance-based Intrusion Detection by GNNs 89 3.6.4 Open Research Challenges 92 3.7 Capability V: LLMs for Intrusion Detection and Understanding 93 3.7.1 The Role of LLMs in Cybersecurity 93 3.

7.2 Leveraging LLMs for Intrusion Detection 94 3.7.3 A Review of LLM-based IDS 94 3.7.4 Open Research Challenges 97 3.8 Summary 97 References 98 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 4.2.1 AI-supported Vulnerability Management 112 4.3 Foundations for Cyber Defense 114 4.3.1 Mapping Vulnerabilities, Weaknesses, and Attack Patterns Using LLMs 115 4.4 Retrieval-augmented Generation 117 4.5 KG and Querying 118 4.

5.1 Graph Schema 119 4.5.2 Neo4j KG Implementation 122 4.5.3 Cypher Queries 123 4.6 Evaluation and Results 126 4.6.

1 RAG-based Response Generation 127 4.6.2 CWE Predictions Using RAG 131 4.6.3 CWE Predictions Using GPT4-o 136 4.7 Conclusion 142 References 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 5.3.1 Blockchain Benefits for Cybersecurity 150 5.3.2 Existing Blockchain-based Cybersecurity Solutions 153 5.4 Combining Generative AI and Blockchain for Cybersecurity 156 5.4.1 Integration of Generative AI and Blockchain 160 5.

4.2 Understanding Capabilities and Risks 160 5.4.3 Practical Benefits for Cybersecurity 161 5.4.4 Limitations and Open Research Issues 161 5.5 Conclusion 162 References 163 6 Privacy-preserving Collaborative Machine Learning 169 Runhua Xu and James Joshi 6.1 Introduction 169 6.

1.1 Objectives and Structure 171 6.2 Collaborative Learning Overview 172 6.2.1 Definition and Characteristics 172 6.2.2 Related Terminologies 174 6.2.

3 Collaborative Decentralized Learning and Collaborative Distributed Learning 175 6.3 Collaborative Learning Paradigms and Privacy Risks 177 6.3.1 Key Collaborative Approaches 177 6.3.2 Privacy Risks in Collaborative Learning 182 6.3.3 Privacy Inference Attacks in Collaborative Learning 183 6.

4 Privacy-preserving Technologies 187 6.4.1 The Need for Privacy Preservation 187 6.4.2 Privacy-preserving Technologies 188 6.5 Conclusion 195 References 196 7 Security and Privacy in Federated Learning 203 Zhuosheng Zhang and Shucheng Yu 7.1 Introduction 203 7.1.

1 Federated Learning 203 7.1.2 Privacy Threats in FL 205 7.1.3 Security Issues in FL 207 7.1.4 Characterize FL 211 7.2 Privacy-preserving FL 215 7.

2.1 Secure Multiparty Computation 215 7.2.2 Trust Execution Environments 216 7.2.3 Secure Aggregation 217 7.2.4 Differential Privacy 218 7.

3 Enhance Security in FL 219 7.3.1 Data-poisoning Attack and Nonadaptive Model-poisoning Attack 220 7.3.2 Model-poisoning Attack 222 7.4 Secure Privacy-preserving FL 225 7.4.1 Enhancing Security in FL with DP 225 7.

4.2 Verifiability in Private FL 226 7.4.3 Security in Private FL 227 7.5 Conclusion 228 References 229 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 8.2.

1 Backdoor Attack Scenarios 239 8.2.2 Attackers'' Capability 240 8.2.3 Attackers'' Objective 240 8.2.4 Targeted ML Models 241 8.3 Attack Formulation and Challenges 241 8.

3.1 Backdoor Attack Formulation 241 8.3.2 Challenges 244 8.4 Poison-label Backdoor Attack 246 8.4.1 Stealthy Trigger Designs 246 8.4.

2 Backdoor Trigger Optimization 249 8.5 Clean-label Backdoor Trigger Design 252 8.5.1 Clean-label Backdoor Trigger Optimization 253 8.6 Evaluation 255 8.6.1 Victim ML Model 255 8.6.

2 Experimental Methodology 255 8.6.3 RF Backdoor Attack Performance 257 8.6.4 Resistance to Backdoor Defense 259 8.7 Related Work 261 8.8 Conclusion 262 References 263 9 Secure by Design 267 Mehdi Mirakhorli and Kevin E. Greene 9.

1 Introduction 267 9.1.1 Definitions and Contexts 268 9.1.2 Core Principles of "Secure by Design" 269 9.1.3 Principle of Compartmentalization and Isolation 273 9.2 A Methodological Approach to Secure by Design 275 9.

2.1 Assumption of Breach 275 9.2.2 Misuse and Abuse Cases to Drive Secure by Design 276 9.2.3 Secure by Design Through Architectural Tactics 277 9.2.4 Shifting Software Assurance from Coding Bugs to Design Flaws 282 9.

3 AI in Secure by Design: Opportunities and Challenges 283 9.4 Conclusion and Future Directions 284 References 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 10.2.1 Network-based Intrusion Detection Systems 295 10.2.2 Host-based Intrusion Detection Systems 300 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 References 314 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 11.2.1 Cybersecurity 320 11.2.2 Data Science 326 11.3 State of the Art 333 11.3.1 Data Acquisition 334 11.

3.2 Data Preparation 335 <.

To be able to view the table of contents for this publication then please subscribe by clicking the button below...

To be able to view the full description for this publication then please subscribe by clicking the button below...