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Journal of Cybersecurity and Information Management
Volume 13 , Issue 1, PP: 60-68 , 2024 | Cite this article as | XML | Html |PDF

Title

Optimizing Intrusion Detection Mechanisms for IoT Network Security

  Ahmed Aziz 1 * ,   Sanjar Mirzaliev 2

1  Tashkent State Universtiy of Economics, Tashkent, Uzbekistan
    (a.mohamed@tsue.uz)

2  Tashkent State Universtiy of Economics, Tashkent, Uzbekistan
    (sanjar2611@gmail.com)


Doi   :   https://doi.org/10.54216/JCIM.130106

Received: May 22, 2023 Revised: August 16, 2023 Accepted: December 19, 2023

Abstract :

The ubiquity of interconnected devices within the Internet of Things (IoT) paradigm has revolutionized modern connectivity, simultaneously amplifying the susceptibility of networks to diverse security threats. This study addresses the pressing necessity for robust intrusion detection mechanisms tailored for IoT networks. Utilizing a simulated dataset reflecting a spectrum of network intrusions within a military environment, the research employs sophisticated methodologies, notably harnessing Decision Tree (DT) algorithms optimized via Grey Wolf Optimization (GWO) for hyperparameter tuning. The investigation meticulously evaluates and refines intrusion detection mechanisms, emphasizing the pivotal role of feature importance analysis in fortifying network security. Results demonstrate the efficacy of the optimized DT algorithm in the precise classification of network traffic, illuminating key attributes instrumental for intrusion detection. These findings underscore the significance of adaptive and interpretable detection strategies in mitigating evolving threats within IoT networks, advocating for resilient approaches to bolster network security.

Keywords :

Intrusion Detection; Internet of Things (IoT); Network Security; Cybersecurity Measures; Threat Detection; Security Frameworks; Intrusion Mitigation; IoT Devices; Cyber Threats; Network Vulnerabilities; Security Optimization; Anomaly Detection; Machine Learning.

References :

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Abdel-Basset, M., Hawash, H., Chakrabortty, R. K., & Ryan, M. J. (2021). Semi-supervised spatiotemporal deep learning for intrusions detection in IoT networks. IEEE Internet of Things Journal


Cite this Article as :
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MLA Ahmed Aziz, Sanjar Mirzaliev. "Optimizing Intrusion Detection Mechanisms for IoT Network Security." Journal of Cybersecurity and Information Management, Vol. 13, No. 1, 2024 ,PP. 60-68 (Doi   :  https://doi.org/10.54216/JCIM.130106)
APA Ahmed Aziz, Sanjar Mirzaliev. (2024). Optimizing Intrusion Detection Mechanisms for IoT Network Security. Journal of Journal of Cybersecurity and Information Management, 13 ( 1 ), 60-68 (Doi   :  https://doi.org/10.54216/JCIM.130106)
Chicago Ahmed Aziz, Sanjar Mirzaliev. "Optimizing Intrusion Detection Mechanisms for IoT Network Security." Journal of Journal of Cybersecurity and Information Management, 13 no. 1 (2024): 60-68 (Doi   :  https://doi.org/10.54216/JCIM.130106)
Harvard Ahmed Aziz, Sanjar Mirzaliev. (2024). Optimizing Intrusion Detection Mechanisms for IoT Network Security. Journal of Journal of Cybersecurity and Information Management, 13 ( 1 ), 60-68 (Doi   :  https://doi.org/10.54216/JCIM.130106)
Vancouver Ahmed Aziz, Sanjar Mirzaliev. Optimizing Intrusion Detection Mechanisms for IoT Network Security. Journal of Journal of Cybersecurity and Information Management, (2024); 13 ( 1 ): 60-68 (Doi   :  https://doi.org/10.54216/JCIM.130106)
IEEE Ahmed Aziz, Sanjar Mirzaliev, Optimizing Intrusion Detection Mechanisms for IoT Network Security, Journal of Journal of Cybersecurity and Information Management, Vol. 13 , No. 1 , (2024) : 60-68 (Doi   :  https://doi.org/10.54216/JCIM.130106)