Meta-Heuristic Optimization Algorithm-Based Hierarchical Intrusion Detection System

Abstract

Numerous network cyberattacks have been launched due to inherent weaknesses. Network intrusion detection is a crucial foundation of the cybersecurity field. Intrusion detection systems (IDSs) are a type of machine learning (ML) software proposed for making decisions without explicit programming and with little human intervention. Although ML-based IDS advancements have surpassed earlier methods, they still struggle to identify attack types with high detection rates (DR) and low false alarm rates (FAR). This paper proposes a meta-heuristic optimization algorithm-based hierarchical IDS to identify several types of attack and to secure the computing environment. The proposed approach comprises three stages: The first stage includes data preprocessing, feature selection, and the splitting of the dataset into multiple binary balanced datasets. In the second stage, two novel meta-heuristic optimization algorithms are introduced to optimize the hyperparameters of the extreme learning machine during the construction of multiple binary models to detect different attack types. These are combined in the last stage using an aggregated anomaly detection engine in a hierarchical structure on account of the model’s accuracy. We propose a software machine learning IDS that enables multi-class classification. It achieved scores of 98.93, 99.63, 99.19, 99.78, and 0.01, with 0.51 for average accuracy, DR, and FAR in the UNSW-NB15 and CICIDS2017 datasets, respectively.