AI-Based Hybrid Anomaly Detection and Behavioral Threat Response Systems: A Comprehensive Review of Advances, Challenges, and Future Directions

Author(s)	Prof. Pankaj Deshmukh, Saiz Momin, Kshitij Thakkar, Tarun Kandarpa
Country	India
Abstract	With the rapid growth of cloud computing, IoT ecosystems, and distributed enterprise networks, cybersecurity threats have become increasingly sophisticated, dynamic, and difficult to detect using traditional methods. Conventional signature-based Intrusion Detection Systems (IDS) are effective against known threats but struggle with zero-day and polymorphic attacks, while anomaly-based systems powered by Machine Learning and Deep Learning offer improved detection of novel attacks but often suffer from high false-positive rates and limited interpretability. This research reviews and analyzes modern hybrid IDS architectures that integrate classical signature-based detection with AI-driven anomaly detection models. The study further explores the incorporation of Explainable AI (XAI) techniques and contextual threat intelligence frameworks such as MITRE ATT&CK to enhance interpretability, reduce alert fatigue, and improve decision-making for security analysts. The paper highlights recent advancements, identifies existing limitations, and outlines future research directions including adaptive learning systems, federated IDS models, and AI-assisted security operations.
Field	Computer > Network / Security
Published In	Volume 17, Issue 1, January-March 2026
Published On	2026-03-21

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