Title: Anomaly-based intrusion detection system using Harris Hawks optimisation with a sigmoid neuron network

Authors: Lenin Narengbam; Shouvik Dey

Addresses: Department of Computer Science and Engineering, National Institute of Technology Nagaland, Chumukedima, Dimapur, 797103, India ' Department of Computer Science and Engineering, National Institute of Technology Nagaland, Chumukedima, Dimapur, 797103, India

Abstract: This study introduces an innovative approach, merging Harris Hawks optimisation (HHO) with a sigmoid neuron network (SN), to enhance anomaly-based intrusion detection systems (ADS) performance. The resultant SN-HHO hybrid model aims to elevate detection rates and lower false positive rates (FPRs) within ADS. Evaluation across five datasets - UNSW-NB15, CIDDS-001, NSL-KDD, AWID3, and CICDDoS2019 - reveals heightened accuracy and faster convergence compared to existing methods. This work underscores the potential synergy of meta-heuristic optimisation and artificial neural networks, offering a promising strategy to fortify IDS performance and reliability, thus presenting a novel direction for advancing anomaly detection practices.

Keywords: intrusion detection system; IDS; neural network; meta-heuristic optimisation; machine learning.

DOI: 10.1504/IJICS.2024.140219

International Journal of Information and Computer Security, 2024 Vol.24 No.1/2, pp.5 - 27

Received: 07 Apr 2023
Accepted: 04 Jan 2024
Published online: 30 Jul 2024 *

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