AI/ML-Powered Framework for Enhanced Network Intrusion Detection using Non-IOC Methods.

Author(s)	Mr. Darshan U, Mr. Chinmaya G P, Mr. Deepak R, Mr. Varun Kumar S, Dr. Shanthi S
Country	India
Abstract	Cybersecurity threats are evolving rapidly, making traditional Indicators of Compromise (IoC)-based detection methods insufficient in identifying sophisticated network intrusions. This project presents an AI/ML-powered framework for enhanced network intrusion detection using Non-IoC methods. Instead of relying on known attack signatures, the framework leverages advanced machine learning algorithms to identify behavioural anomalies and deviations in network traffic patterns. By analyzing various data points—such as system logs, network flow anomalies, and user behaviour—the proposed system can detect early signs of compromise without prior knowledge of attack signatures. The AI-driven approach ensures adaptive learning, enabling it to recognize emerging threats while minimizing false positives.
Keywords	Network Security, Artificial Intelligence, Machine Learning, Non-IoC Detection, Intrusion Detection, Behavioural Analysis, Anomaly Detection, Cyber Threats, Security Framework, Threat Intelligence.
Field	Computer Applications
Published In	Volume 7, Issue 2, March-April 2025
Published On	2025-04-10
DOI	https://doi.org/10.36948/ijfmr.2025.v07i02.41159
Short DOI	https://doi.org/g9fcb6

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International Journal For Multidisciplinary Research