Gurunathan K, Parthasarathy SM, Vikash O (2026)
Publication Type: Authored book
Publication year: 2026
Publisher: CRC Press
ISBN: 9781042018222
Autonomous systems in transportation, defense, manufacturing, and healthcare face escalating cyber risks due to their data-driven decision-making in complex environments. This paper proposes an AI-driven end-to-end security solution with adaptive risk management and real-time threat detection. Machine and deep learning models monitor behavior, detect anomalies, and predict attacks, combining threat intelligence with contextual awareness for continuous learning and proactive defenses. Dynamic risk ranking prioritizes vulnerabilities, enabling mitigation against zero-day exploits and malware variants. A layered architecture integrates behavioral analysis and network monitoring for autonomic threat response. Trained on real-world attack data, models achieve high accuracy and low false positives, with feedback loops for ongoing refinement. Results show superior detection speed, accuracy, and recovery over traditional methods, ensuring robust, compliant operation in mission-critical applications.
APA:
Gurunathan, K., Parthasarathy, S.M., & Vikash, O. (2026). Strengthening Cybersecurity in Autonomous Systems: Adaptive Risk Management and Real-Time Threat Detection Using AI. CRC Press.
MLA:
Gurunathan, Karthikeyan, Sandhya Murali Parthasarathy, and Om Vikash. Strengthening Cybersecurity in Autonomous Systems: Adaptive Risk Management and Real-Time Threat Detection Using AI. CRC Press, 2026.
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