AI-Enabled Threat Detection in Network Security

  • Authors

    • Dr. Rajesh Kumar Sharma Professor, University of Delhi, India. Author

    DOI:

    https://doi.org/10.67228/30715628/IJMIET-2022PI3W7Z

    Published 04-05-2022

  • Artificial Intelligence, Network Security, Intrusion Detection System, Machine Learning, Deep Learning, Cyber Threat Detection

    Issue

    Vol. 5 No. 1 (2022)

    Section

    Articles

    How to Cite

    [1]
    R. K. Sharma, “AI-Enabled Threat Detection in Network Security”, ijmiet, vol. 5, no. 1, pp. 01–14, Apr. 2022, doi: 10.67228/30715628/IJMIET-2022PI3W7Z.

  • Abstract

    The expansion in the domains of digital communication, cloud computing, Internet of Things (IoT), and 5G technologies has greatly increased the attack area of the contemporary network infrastructure. The conventional network security measures, which rely mostly on fixed rules and signature-based responses, are becoming incapable of responding to more advanced, changing and zero-day cyber-attacks. Artificial Intelligence (AI) has become a disruptive technology that can be used to improve network security through enhanced and real-time and adaptive threat issues. This paper consists of an in-depth analysis of AI-powered threat detection when it is applied to guarantee network security, its principles, techniques, methodology, and the performance results. The paper is started with the discussion of the limitations of traditional intrusion detection and prevental systems and the necessity of intelligent automation in security problems. It has been thoroughly analyzed with the literature review of the modern progress in the fields of machine learning, deep learning, and hybrid AI applications to network threat detection. The suggested methodology is an AI-based design that includes the process of data gathering, feature engineering, model training, and classifying the threats. Several types of machine learning algorithms are analyzed with reference to their usefulness in identifying anomalous and malicious network behavior, such as supervised, unsupervised and deep learning models. The experimental analysis shows that AI threatened detection systems can greatly increase the accuracy of detection, a decrease in false positives and an increase in the response time in comparison to the usual methods. This discussion examines the performance metrics, scalability, and deployment issues in the actual environments. Lastly, the paper has come to an end by summarizing the major findings and stating the direction of future research moves, which will focus on the role of explainable AI, federated learning, and adaptive defense mechanisms in the next-generation network security systems.

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