aspg-3-3660-1791419457 20261008003057 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2025 19 2 Vasanth Nayak Research Scholar, Department of Computer Science and Engineering, Nitte (Deemed to be University) NMAM Institute of Technology, Nitte (DU), SH1, Karkala, Karnataka 574110, India; Department o Sumathi Pawar Associate Professor, Department of Information Science and Engineering, NMAMIT Nitte (DU), Karkala, Karnataka 574110, India Sunil Kumar B. L. Associate Professor, Department of Computer Science and Engineering, Canara Engineering College, Mangaluru, Karnataka 574219, India Network intrusions are becoming more common, resulting in significant privacy violations, financial losses, and the illegal transfer of sensitive information. Numerous intrusion strategies pose a threat to data, computer resources, and networks. While hackers may focus on obtaining trade secrets, private information, or confidential data that can then be disclosed for illegal purposes, each type of intrusion aims to achieve a distinct objective. False attack detection by security systems and changing threat environments create challenges such as delayed identification of true attacks and long-term financial harm. This paper presents a novel hybrid optimization algorithm-assisted deep learning model for accurately identifying intrusion types and enhancing network security. Initially, input information is composed from openly obtainable datasets. The input data is cleaned, normalized, and standardized to produce accurate results. An improved synthetic minority oversampling technique (ISMOTE) for data balance reduces the method's overfitting problem. Then, the Chaotic Bat Artificial Bee Colony optimization algorithm (CBABCOA) is used to identify critical features and reduce feature dimensionality issues. HSVM-XGBoost (Hybrid Kernel Support Vector Machine-Extreme Gradient Boosting) is used for intrusion detection and classification. The Chaotic Binary Horse Optimization Algorithm (CBHOA) is used for hyper parameter tuning. This method makes use of the CIC UNSW-NB15 Augmented dataset, the CICIDS 2019 data set, and the NSL-KDD information set. The proposed method achieves better than the other method. 2025 45 63 3660 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.190204 https://www.americaspg.com/journal/3/article/3660 https://www.americaspg.com/storage/21742486009.pdf