AI for Cybersecurity : Research and Practice

by a team of highly qualified experts in the field, AI for Cybersecurity discusses topics including: Robustness and risks of the methods covered, including adversarial ML threats in model training, deployment, and reuse Privacy risks including model inversion, membership inference, attribute inference, re-identification, and deanonymization Forensic and formal methods for analyzing, auditing, and verifying security- and privacy-related aspects of AI components Use of generative AI systems for improving security and the risks of generative AI systems to security Transparency and interpretability/explainability of models and algorithms and associated issues of fairness and bias AI for Cybersecurity is an excellent reference for practitioners in AI for cybersecurity related industries such as commerce, education, energy, financial services, healthcare, manufacturing, and defense. Fourth year undergraduates and postgraduates in computer science and related programs of study will also find it valuable

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