An analysis of alternative machine learning and deep learningalgorithms for categorization and detection of various active ‎network assaults

  • Authors

    • 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
    https://doi.org/10.14419/zywhgb37

    Received date: May 4, 2025

    Accepted date: May 23, 2025

    Published date: May 26, 2025

  • Cyber-Attacks; DDoS; IDS; Machine Learning; Intrusion Detection; Deep Learning; Network Attacks

  • Abstract

    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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    Kaliyaperumal, D. K., Boddu, P. R. S. K. ., & Oruganti, P. S. K. . (2025). An analysis of alternative machine learning and deep learningalgorithms for categorization and detection of various active ‎network assaults. International Journal of Basic and Applied Sciences, 14(1), 414-421. https://doi.org/10.14419/zywhgb37