Explainable AI for Cybersecurity

• Autore: Pan Zhixin
• Pubblicazione: Cham : , : Springer International Publishing AG, , 2023; ©2023
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (249 pages)
• Soggetto topico: Computer security; Artificial intelligence
• Altri autori: MishraPrabhat
• Nota di contenuto: Intro -- Preface -- Acknowledgements -- Contents -- Acronyms -- Part I Introduction -- Cybersecurity Landscape for Computer Systems -- 1 Introduction -- 2 Cybersecurity Vulnerabilities -- 2.1 Hardware Vulnerabilities -- 2.1.1 Malicious Implants (Hardware Trojans) -- 2.1.2 Supply Chain Vulnerability -- 2.1.3 Reverse Engineering -- 2.1.4 Side-Channel Leakage -- 2.2 Software Vulnerabilities -- 2.2.1 Malware Attacks -- 2.2.2 Ransomware Attacks -- 2.2.3 Spectre and Meltdown Attacks -- 2.3 Malicious Attacks on Machine Learning Models -- 2.3.1 Adversarial Attacks -- 2.3.2 AI Trojan Attacks -- 3 Detection of Security Vulnerabilities -- 3.1 Detection of Malicious Hardware Attacks -- 3.1.1 Simulation-Based Validation Using Machine Learning -- 3.1.2 Side-Channel Analysis Using Machine Learning -- 3.1.3 Heuristic Analysis Using Machine Learning -- 3.2 Detection of Malicious Software Attacks -- 3.2.1 Detection of Malware Attacks -- 3.2.2 Detection of Ransomware Attacks -- 3.2.3 Detection of Spectre and Meltdown Attacks -- 4 Summary -- References -- Explainable Artificial Intelligence -- 1 Introduction -- 2 Machine Learning Models -- 2.1 Support Vector Machine -- 2.2 Multi-Layer Perceptron -- 2.3 Decision Tree -- 2.4 Random Forest -- 2.5 Linear Regression -- 2.6 Deep Neural Network -- 2.7 Convolution Neural Network -- 2.8 Recurrent Neural Network -- 2.9 Long Short-Term Memory -- 2.10 Reinforcement Learning -- 2.11 Boosting -- 2.12 Naive Bayes -- 2.13 Zero-Shot Learning -- 3 Explainable Artificial Intelligence -- 3.1 Local Interpretability -- 3.2 Knowledge Extraction -- 3.3 Saliency Maps -- 3.4 Integrated Gradients -- 3.5 Shapley Value Analysis -- 3.6 Layer-Wise Relevance Propagation -- 4 Summary -- References -- Part II Detection of Software Vulnerabilities -- Malware Detection Using Explainable AI -- 1 Introduction -- 2 Background and Related Work. ; 2.1 Malware Detection Challenges -- 2.2 Why Explainable AI for Malware Detection? -- 3 Malware Detection Using Explainable Machine Learning -- 3.1 Model Training -- 3.2 Perturbation and Outlier Elimination -- 3.3 Linear Regression -- 3.4 Outcome Interpretation -- 4 Experiments -- 4.1 Experimental Platform -- 4.2 Malware and Benign Benchmarks -- 4.3 Data Acquisition -- 4.4 RNN Classifier -- 4.5 Evaluation: Accuracy -- 4.6 Evaluation: Outcome Interpretation -- 5 Summary -- References -- Spectre and Meltdown Detection Using Explainable AI -- 1 Introduction -- 1.1 Threat Model -- 1.2 Motivation -- 2 Background and Related Work -- 2.1 Spectre and Meltdown Attacks -- 2.2 Explainable Machine Learning -- 3 Detection of Spectre and Meltdown Attacks -- 3.1 Data Collection -- 3.2 Model Training -- 3.2.1 LSTM-Based Model Training -- 3.2.2 Ensemble Boosting -- 3.3 Result Interpretation -- 3.3.1 Explainability Using Model Distillation -- 3.3.2 Explainability Using Shapely Values -- 3.4 Data Augmentation -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Comparison with Existing Spectre Detection Methods -- 4.3 Comparison with Existing Meltdown Detection Methods -- 4.4 Comparison with Existing Mitigation Techniques -- 4.5 Stability Analysis -- 4.6 Explainability Analysis -- 4.7 Efficiency Analysis -- 5 Summary -- References -- Part III Detection of Hardware Vulnerabilities -- Hardware Trojan Detection Using Reinforcement Learning -- 1 Introduction -- 2 Background and Related Work -- 2.1 Logic Testing for Hardware Trojan Detection -- 2.2 Reinforcement Learning -- 3 Test Generation Using Reinforcement Learning -- 3.1 Identification of Rare Nodes -- 3.2 Testability Analysis -- 3.3 Utilization of Reinforcement Learning -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Results on Trigger Coverage -- 4.3 Results on Test Generation Time -- 5 Summary -- References. ; Hardware Trojan Detection Using Side-Channel Analysis -- 1 Introduction -- 2 Background and Motivation -- 2.1 Background: Reinforcement Learning -- 2.2 Motivation: Delay-Based Side-Channel Analysis -- 3 Reinforcement Learning-Based Path Delay Analysis -- 3.1 Overview -- 3.2 Generation of Initial Vectors -- 3.3 Generation of Succeeding Vectors -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Evaluation Results -- 5 Summary -- References -- Hardware Trojan Detection Using Shapley Ensemble Boosting -- 1 Introduction -- 2 Background and Related Work -- 2.1 Related Work for Hardware Trojan Detection -- 2.2 Ensemble Boosting -- 2.3 Shapley Values -- 3 Shapley Ensemble Boosting for Hardware Trojan Detection -- 3.1 Data Sampling -- 3.2 Model Training -- 3.3 Shapley Analysis -- 3.4 Weight Adjustment -- 3.5 Ensemble Prediction -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 HT Detection Performance -- 4.3 Explainability Analysis -- 4.4 Efficiency Analysis -- 4.5 Robustness Analysis -- 5 Summary -- References -- Part IV Mitigation of AI Vulnerabilities -- Mitigation of Adversarial Machine Learning -- 1 Introduction -- 2 Background and Preliminaries -- 2.1 Attacks on Neural Networks -- 2.2 Spectral Normalization -- 3 Spectral Normalization to Defend Against Adversarial Attacks -- 3.1 Layer Separation -- 3.2 Fourier Transform -- 3.3 Activation Functions -- 3.4 Complexity Analysis -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Case Study: MNIST Benchmark -- 4.3 Case Study: ImageNet Benchmark -- 5 Summary -- References -- AI Trojan Attacks and Countermeasures -- 1 Introduction -- 2 Background and Related Work -- 3 Backdoor Attack with AI Trojans -- 3.1 Feature Extraction -- 3.2 Normal Training -- 3.3 Backdoor Training -- 3.4 Trojan Injection -- 4 Defenses Against AI Trojans -- 4.1 Pruning -- 4.2 Bayesian Neural Networks -- 4.3 Neural Cleanse. ; 4.4 Artificial Brain Stimulation (ABS) -- 4.5 STRIP -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Comparison of Attack Performance -- 5.3 Overhead Analysis -- 5.4 Robustness Against STRIP-Based Defense -- 6 Summary -- References -- Part V Acceleration of Explainable AI -- Hardware Acceleration of Explainable AI -- 1 Introduction -- 1.1 Graphics Processing Unit -- 1.2 Field Programmable Gate Array -- 2 FPGA-Based Acceleration of Heatmap Visualization -- 3 FPGA-Based Acceleration of Saliency Map -- 4 GPU-Based Acceleration of Shapley Value Analysis -- 4.1 Summary -- References -- Explainable AI Acceleration Using Tensor Processing Units -- 1 Introduction -- 2 Tensor Processing Units -- 3 Hardware Acceleration of Explainable AI -- 3.1 Task Transformation -- 3.2 Data Decomposition in Fourier Transform -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Comparison of Accuracy and Classification Time -- 4.3 Comparison of Energy Efficiency -- 5 Summary -- References -- Part VI Conclusion -- The Future of AI-Enabled Cybersecurity -- 1 Introduction -- 2 Summary -- 2.1 Introduction to Cybersecurity and Explainable AI -- 2.1.1 Detection of Software Vulnerabilities -- 2.2 Detection of Hardware Vulnerabilities -- 2.3 Mitigation of AI Vulnerabilities -- 2.4 Acceleration of Explainable AI -- 3 Future Directions -- 3.1 Automatic Implementation of Secure Systems -- 3.2 Detection of Malicious Implants -- 3.3 Detection of Ransomware Attacks -- 3.4 Automatic Data Augmentation -- References -- Index.
• Sommario/riassunto: This book provides a comprehensive examination of the role of Explainable Artificial Intelligence (AI) in enhancing cybersecurity. It addresses the growing importance of AI in defending against sophisticated cyber threats while highlighting the challenges posed by its black-box nature. The authors, Zhixin Pan and Prabhat Mishra, explore various machine learning algorithms and explainable AI techniques to detect and mitigate both software and hardware security threats, such as malware and hardware Trojans. Additionally, the book discusses hardware acceleration techniques using FPGA, GPU, and TPU to enhance the efficiency of explainable AI systems. The work is intended for researchers, practitioners, and students in the fields of cybersecurity and AI, offering insights from academic research and industrial collaborations. The book aims to guide the design of secure, trustworthy computing systems in an era of advanced technological threats.
• Titolo autorizzato: Explainable AI for Cybersecurity
• ISBN: 9783031464799; 3031464796
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910770274903321
• Lo trovi qui: Univ. Federico II