International Journal of Science, Engineering and TechnologyAuthors: Muskan, Dr. Yatu Rani
Abstract: The evolution of cyber threats requires security methods that are smarter, more adaptive, and tailored to the unique properties of web technology beyond the capabilities of traditional IDS. AegisIDS [10] – An adaptive hybrid intru-sion detection system combining signature based and machine learning-driven anomaly detection for greater accuracy and responsiveness. The new system has been proposed using several techniques such as dynamic data sampling technique, optimized feature selection, and ensemble learning to solve problems related to class imbalance, false positive rate and detection latency. AegisIDS is built to work well for today environments including Cloud, Internet of Things (IoT), and enterprise network. Ex-perimental insights from recent hybrid IDS studies demonstrate that combining adaptive learning with hybrid architectures significantly improves detection rates and reduces false alarms. This paper discusses the architecture, methodology, performance considerations, and future scope of AegisIDS.
DOI: https://doi.org/10.5281/zenodo.20227328
