[LGPR] Publication Successes Dr.Karthikeyan Kaliyaperumal

Dr Karthikeyan Kaliyaperumal, Ambo University, Ambo, Nigeria

International Journal of Basic and Applied Sciences, 14 (1) (2025) 414-421 www.sciencepubco.com/index.php/IJBAS

An analysis of alternative machine learning and deep learning algorithms for categorization and detection of various active ‎network assaults https://doi.org/10.14419/zywhgb37

Author Details:

Dr. Karthikeyan Kaliyaperumal

Post Doc Researcher, Lincoln University College, Malaysia

Prof. Raja Sarath Kumar Boddu

Professor and Head of the Department of IT, School of Engineering, Malla Reddy University, Hyderabad, India

Prof. Sai Kiran Oruganti

Faculty of Engineering and Built Science, Lincoln University College-KL Malaysia

Abstract

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Attacks on networks have grown increasingly widespread because of the exponential growth in internet traffic and the rapid progress of ‎network technology. A network attack occurs when a person gains illegal entry into a network. This includes any attempt to destroy the network, which might have disastrous consequences. Organizations depend significantly on tried-and-true network infrastructure security fea-‎tures like firewalls, encryption, and antivirus software. However, these strategies provide some defence against increasingly sophisti-‎cated attacks and viruses. Machine learning (ML) and deep learning (DL) are two important key concepts of artificial intelligence that gained ‎popularity around the turn of the century. The focus on statistical methodologies and data in these techniques may considerably improve ‎computing power by training computers to think like people. So, to address the inadequacies of non-intelligent solutions, computer ‎scientists started to use intelligent approaches in network security. This article provides a thorough examination of numerous deep learning ‎and machine learning methods for attack detection and classification.

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