Machine Learning-Based Cooperative Detection Method for Effectively Mitigating Availability Attacks in Wireless Network Environments 


Vol. 14,  No. 9, pp. 662-667, Sep.  2025
https://doi.org/10.3745/TKIPS.2025.14.9.662


  Abstract

With the rapid increase in the number of Internet of Things (IoT) devices such as smartphones, wearables, and smart appliances, data transmission over wireless networks has significantly grown. Consequently, availability attacks targeting wireless networks—such as jamming, flooding, blackhole, and grayhole attacks—are also rising. Conventional attack detection methods rely on statistical indicators such as packet delivery rate, received signal strength, and timestamps. However, these approaches depend heavily on resource-constrained IoT devices, resulting in low detection accuracy and degradation of overall network performance. Recently, machine learning-based local detection methods have been explored, but their high computational complexity makes them unsuitable for deployment on lightweight devices. This paper proposes a cooperative detection framework that distributes machine learning-based training and detection tasks between resource-rich access points (APs) and lightweight IoT devices. Experimental results show that the proposed model improves detection accuracy by an average of 38% and data transmission success rate by 17.75% compared to conventional local learning-based detection models.

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  Cite this article

[IEEE Style]

M. Cho, S. Jeon, I. Lee, "Machine Learning-Based Cooperative Detection Method for Effectively Mitigating Availability Attacks in Wireless Network Environments," The Transactions of the Korea Information Processing Society, vol. 14, no. 9, pp. 662-667, 2025. DOI: https://doi.org/10.3745/TKIPS.2025.14.9.662.

[ACM Style]

Min-Ji Cho, So-Eun Jeon, and Il-Gu Lee. 2025. Machine Learning-Based Cooperative Detection Method for Effectively Mitigating Availability Attacks in Wireless Network Environments. The Transactions of the Korea Information Processing Society, 14, 9, (2025), 662-667. DOI: https://doi.org/10.3745/TKIPS.2025.14.9.662.

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