An Adaptive Hybrid MFO–TLBO Framework for Multi-Objective Optimization in Network Intrusion Detection Systems
Year: 2026
Type: article
Abstract: This preprint presents an adaptive hybrid MFO–TLBO optimization framework integrated with Extreme Learning Machine (ELM) for network intrusion detection. The proposed method combines the exploration capability of Moth-Flame Optimization (MFO) with the exploitation strength of Teaching-Learning-Based Optimization (TLBO) to enhance feature selection and classification performance. Experimental evaluation on the KDDCUP1999 dataset demonstrates that the proposed hybrid approach achieves 95.19% detection accuracy, outperforming several standalone optimization-based IDS models. This version represents a preliminary preprint. An extended version including evaluation on modern datasets such as CICIDS2018 will be submitted for journal publication.
Author: Alaasam, Mohammed Kadhim Radhi
Institution: Islamic Azad University South Tehran Branch
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7128710022
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