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Leverage AI for adaptive cyber defense against evolving threats Artificial intelligence simultaneously strengthens cyber defenses and introduces new attack vectors such as adversarial exploits and AI-driven malware. AI-Driven Security: Navigating Threats and Defenses in a New Era , written by two cybersecurity researchers specializing in digital authentication and information assurance, examines this dual role by integrating machine learning, deep learning, and generative AI across networks, endpoints, IoT, and autonomous systems. AI-Driven Security covers real-time network traffic analysis and anomaly detection, insider threat identification through pattern analysis, and AI-assisted incident response that reduces delays. The book examines adversarial attacks on AI systems and strategies for building resilient defenses. It also addresses ethical dimensions including privacy, bias and fairness, and accountability alongside regulatory and societal considerations. Readers will also find: Real-world case studies illustrating how AI accelerates detection and mitigation during cyber incidents across diverse operational environments Coverage of emerging technologies including 6G networks, quantum computing challenges, and blockchain integration with privacy-enhancing security frameworks Strategies for defending against adversarial attacks targeting AI models and securing machine learning pipelines from exploitation Analysis of ethical issues in AI-driven security spanning bias, fairness, transparency, and accountability in automated decision-making Forward-looking perspectives on generative AI, reinforcement learning, and their roles in proactive threat prediction and response Designed for cybersecurity professionals, AI researchers, and policymakers, this book connects threat detection automation with broader defense frameworks and ethical governance. Students and academics in AI and cybersecurity programs will also find structured, research-grounded coverage of both offensive and defensive AI applications.
Leverage AI for adaptive cyber defense against evolving threats Artificial intelligence simultaneously strengthens cyber defenses and introduces new attack vectors such as adversarial exploits and AI-driven malware. AI-Driven Security: Navigating Threats and Defenses in a New Era , written by two cybersecurity researchers specializing in digital authentication and information assurance, examines this dual role by integrating machine learning, deep learning, and generative AI across networks, endpoints, IoT, and autonomous systems. AI-Driven Security covers real-time network traffic analysis and anomaly detection, insider threat identification through pattern analysis, and AI-assisted incident response that reduces delays. The book examines adversarial attacks on AI systems and strategies for building resilient defenses. It also addresses ethical dimensions including privacy, bias and fairness, and accountability alongside regulatory and societal considerations. Readers will also find: Real-world case studies illustrating how AI accelerates detection and mitigation during cyber incidents across diverse operational environments Coverage of emerging technologies including 6G networks, quantum computing challenges, and blockchain integration with privacy-enhancing security frameworks Strategies for defending against adversarial attacks targeting AI models and securing machine learning pipelines from exploitation Analysis of ethical issues in AI-driven security spanning bias, fairness, transparency, and accountability in automated decision-making Forward-looking perspectives on generative AI, reinforcement learning, and their roles in proactive threat prediction and response Designed for cybersecurity professionals, AI researchers, and policymakers, this book connects threat detection automation with broader defense frameworks and ethical governance. Students and academics in AI and cybersecurity programs will also find structured, research-grounded coverage of both offensive and defensive AI applications.
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