2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 15 , Issue 2 , PP: 76-90, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
K. Anitha 1 * , K. Rajiv Gandhi 2 *
Doi: https://doi.org/10.54216/JISIoT.150206
The initial identification of cybersecurity events like attacks is challenging provided the continuously growing threat environment. Despite state-of-the-art surveillance, advanced attackers can apply for more than 100 days in a system before being detected. Guaranteeing cyber security is a composite task that depends on area of interest and needs cognitive capabilities to control possible threats from larger quantities of network data. The most important task of a cyber-security analyst is to safeguard a network from damage. Numerous technological developments in network and information security have enabled progressive monitoring and threat detection for the predictors, but the responsibilities they carried out could not be automated completely. Hence, in recent times’ Artificial intelligence (AI), mainly deep learning (DL) and machine learning (ML) algorithms, has been utilized to expand a beneficial data-driven intrusion detection system (IDS). Many standard ML classification methods provide intelligent facilities in the area of cyber-security, mainly for intrusion detection. This study develops a Tuna Swarm Optimization-Driven Feature Selection with Ensemble of Machine Learning Models for Cybersecurity Threat Detection and Classification (TSOFSEML-CTDC) technique. The proposed TSOFSEML-CTDC model concentrates on detecting and classifying intrusions on the network. Initially, the TSOFSEML-CTDC algorithm performs data preprocessing using min-max normalization to convert an input data into a beneficial format. Then, the feature selection process has been carried out using tuna swarm optimization (TSO) algorithm. For the classification of intrusion detection, ensemble of ML techniques was employed such as support vector regression (SVR) approach, least-square support vector machines (LSSVM) method, and modified extreme learning machine (MELM) technique. At last, the hyperactive parameter optimization process is executed by using the coati optimization algorithm (COA). The experimental evaluation of the TSOFSEML-CTDC model occurs using a benchmark dataset. The stimulated results emphasized the enhanced performance of the TSOFSEML-CTDC method compared to existing approaches.
Tuna Swarm Optimization , Ensemble of Machine Learning , Cybersecurity Threat Detection , Coati Optimization Algorithm , Data Preprocessing
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