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.

Rhizostoma optimization algorithm and its application in different real-world optimization problems

“…It can be figured as a local search. The way in which the meta-heuristics balance these two main processes is the challenge that differentiate a meta-heuristic from another [7]. The main aim of this study is to develop a novel meta-heuristic by inspiring the social behavior of Rhizostoma octopus in the ocean, including their food searching, and their moves inside the swarm.…”

“…Algorithms such as the grey wolf optimizer (GWO) [1], the whale optimization algorithm (WOA) [2], the Jellyfish search (JS) [3], the ant lion optimizer (ALO) [4], the archimedes optimization algorithm (AOA) [5], and particle swarm optimization (PSO) [6] are successful examples for these techniques which used efficiently in scientific research. Five main reasons make the meta-heuristics remarkably common [7]: simplicity because of the very simple concepts their inspiration based on, flexibility because of the stability in their structure with the applicability in different issues, derivation-free mechanism because of the random solution(s) their process starts with [8], and finding the optimum do not need any calculations to derive the search spaces, local optima avoidance due to their stochastic nature which allows them to avoid falling into local solutions and getting to the global solution, beside the ease of their implementation [3]. These make meta-heuristics considering as highly suitable and a good option for real problems.…”

Light-Weight Slow-Rate Attack Detection Framework for Resource-Constrained Industrial Cyber-Physical Systems

“…Several works utilise shallow deep learning (SDL) models for classification because of their proven online learning detection capabilities, less training time and computational efficiency [23,24,39]. The works [24,23] are based on traditional Extreme Learning Machine (ELM). ELM has certain limitations regarding training efficiency, memory requirements, and applicability.…”

An Optimized Intelligent Open-Source MLaaS Framework for User-Friendly Clustering and Anomaly Detection

“…This data can be extremely valuable to businesses if analyzed and utilized correctly. Machine learning algorithms have proven to be highly effective in analyzing data in various domains, including business [1], medicine [2][3][4] , communication [5],intrusion detection [6], and industry [7], making them useful for data collection and analysis. With the help of these algorithms, businesses can gain more valuable insights, identify patterns, and build a deeper understanding of the collected data.…”