This monograph covers intelligent threat detection, behavioral analytics, automated incident response, threat intelligence, security orchestration, adversarial machine learning, API security, Infrastructure-as-Code security, privacy-preserving AI, threat actor profiling, and intelligent Security Operations Center architectures.
Abstract
Advances in Cybersecurity: Artificial Intelligence, Automation, and Digital Defense explores emerging cybersecurity technologies that use artificial intelligence, machine learning, and automation to address evolving cyber threats. The monograph covers intelligent threat detection, behavioral analytics, automated incident response, threat intelligence, security orchestration, adversarial machine learning, API security, Infrastructure-as-Code security, privacy-preserving AI, threat actor profiling, and intelligent Security Operations Center architectures. It highlights how AI-driven approaches improve threat prediction, security monitoring, response efficiency, and digital resilience. The book provides researchers, academicians, students, and cybersecurity professionals with contemporary concepts, practical approaches, and future directions for developing intelligent, adaptive, and robust digital defense systems.
Artificial Intelligence and Cybersecurity: Innovations, Challenges, and Applications explores the transformative role of artificial intelligence in modern cybersecurity. The monograph examines how AI, machine learning, deep learning, and intelligent analytics support threat detection, attack prediction, security monito...
Cybersecurity Transformation: Artificial Intelligence, Cloud, Data, and Digital Resilience examines the transformation of modern cybersecurity through artificial intelligence, cloud computing, data-driven security, and resilient digital architectures. The monograph explores intelligent threat detection, machine learnin...
The findings indicate that AI can improve the speed, scalability, adaptability, and proactive capabilities of cybersecurity systems, however, challenges including data quality, false positives and negatives, adversarial attacks, privacy risks, lack of explainability, computational requirements, and ethical concerns rem...
Gurwinder Singh· International Journal of Sci...· 0 citations
Artificial intelligence (AI) has emerged as a transformative force in cybersecurity, offering capabilities that extend far beyond the static, rule-based defenses of the past. Machine learning, deep learning, and natural language processing techniques are increasingly embedded in intrusion detection systems, threat inte...
Nicolas Guzman Camacho· Journal of Artificial Intell...· 0 citations
The review finds that artificial intelligence enables proactive threat detection, anomaly identification, and the automation of analysis and response at a scale beyond human capacity, yet its effectiveness is constrained by adversarial machine learning, data quality and drift, false positives, opacity, and the dual use...
N. Hussain· WORLD JOURNAL OF INNOVATION...· 0 citations
Modern cyber threats evolve faster than static, signature-driven defenses can respond, creating a need for security architectures that can continuously observe, interpret, predict, and adapt to changing attack behavior. This talk presents an adaptive AI-driven cybersecurity approach that combines real-time security tel...
Kalyana Krishna Kondapalli· Proceedings of International...· 0 citations
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