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Gonda, Arumugam, Doss, Srinath, Virdee, Bal Singh and Khanna, Ashish (2026) Wide Slice Kronecker Network for concept drift aware malware variant traffic identification at an IoT edge gateway. Journal of Parallel and Distributed Computing (105285). pp. 1-52. ISSN 0743-7315
The Internet of Things (IoT) is an advancing technology that facilitates the connection of a wide range of devices used in various aspects of daily living. In recent years, the security of IoT has become crucial because of the continuous growth of IoT devices and wireless communications. Monitoring malicious activities in IoT systems, particularly at the edge gateway, is critical to ensure security. Existing schemes for detecting malware variant traffic often fail to accurately classify variants and cannot adapt to concept drift, leaving systems vulnerable to evolving threats. To overcome these limitations, this paper introduces the Wide Slice Kronecker Network (WSliceKN) model, designed specifically to detect malware variant traffic in IoT networks. The approach begins by extracting both traffic-based and network-based features from log data, after which data augmentation is performed using an oversampling method. Afterward, malware variants are detected using WSliceKN, with concept drift monitored through an adaptive sliding window. Following drift detection, the model undergoes retraining using a combination of mutual information and a loss function. Extensive experiments are conducted, where WSliceKN outperforms existing schemes with accuracy, F1-score, recall, and precision of 94.438%, 94.622%, 95.941%, and 93.729%, respectively.
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