Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{196371,
        author = {Tharala Mallishwari and Padamati Hamshika Reddy and Dasari Raju and Vinayak Biradar},
        title = {AI-Powered Intrusion Detection System with Hybrid Bat-Grey Wolf Optimization},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {3863-3873},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196371},
        abstract = {Along with the pace increase of the advanced cyber threats, for the development of a robust, smart network. monitoring systems. Traditional intrusion detection systems operate by Intrusion Detection Systems-IDS that use rule-based and signature-based methods tend to have low accuracy in unknown or zero- day attacks and, eventually leading to an increase in false positive rates. This paper should help to address these challenges by introducing an - Bat-Grey Hybrid-optimized Artificial Intelligence-based IDS Wolf Optimizer (HB-GWO) method. In this hybrid optimization method, the HB-GWO optimizes feature selection along with machine learning classifier hyperparameters by combining the the efficient exploration capabilities of Bat Algorithm with Grey Wolf. Optimizer’s effective exploitation capabilities. Experimental anal- ysis on the benchmark datasets, namely NSL-KDD and CICIDS2017. has been carried out, confirming improved results in intrusion detection. Detection accuracy, precision, recall, F1-measure, in addition to from these results, it follows that the false positive rates were reduced by a large margin. Experimental Results confirm that optimized IDS using HB-GWO performs well in terms of accuracy and F1 score. performs better than the conventional optimization methods along with with competitive classifiers in terms of convergence efficiency and accuracy concerning intrusion detection capabilities. In In this paper, an optimized IDS framework has been proposed using the HB- GWO is robust, adaptive, real-time, cloud-friendly, and it aims at It improves cybersecurity with integrated artificial intelligence. development and optimization capabilities.},
        keywords = {Intrusion Detection System (IDS), Artificial Intel-Intelligence), Bat Algorithm, Grey Wolf Optimizer, Hybrid Optimization, Feature Selection, Cybersecurity, Meta- Heuristic algorithms, NSL-KDD.},
        month = {April},
        }