Tedyyana, Agus and Ghazali, Osman and Purbo, Onno W. and Abu Seman, Mohamad Amir (2024) Enhancing intrusion detection system using Rectified Linear Unit function in Pigeon-Inspired Optimization algorithm. IAES International Journal of Artificial Intelligence (IJ-AI), 13 (2). pp. 1526-1534. ISSN 2252-8938

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Abstract

The increasing rate of cybercrime in the digital world highlights the importance of having a reliable intrusion detection system (IDS) to detect unauthorized attacks and notify administrators. IDS can leverage machine learning techniques to identify patterns of attacks and provide real-time notifications. In building a successful IDS, selecting the right features is crucial as it determines the accuracy of the predictions made by the model. This paper presents a new IDS algorithm that combines the rectified linear unit (ReLU) activation function with a pigeon-inspired optimizer in feature selection. The proposed algorithm was evaluated on network security layer - knowledge discovery in databases (NSL-KDD) datasets and demonstrated improved performance in terms of training speed and accuracy compared to previous IDS models. Thus, the use of the ReLU activation function and a pigeon-inspired optimizer in feature selection can significantly enhance the effectiveness of an IDS in detecting unauthorized attacks.

Item Type:	Article
Uncontrolled Keywords:	Feature selection; Intrusion detection system; Machine learning; neural network; Pigeon inspired optimization; Rectified linear unit
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics
Divisions:	School of Computing
Depositing User:	Mr. Izhar Hj. Hashim
Date Deposited:	04 Nov 2026 06:37
Last Modified:	04 Nov 2026 06:37
URI:	https://repo.uum.edu.my/id/eprint/34609

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