Edge-based IIoT Malware Detection for Mobile Devices with Offloading

Document Type

Article

Publication Date

10-25-2022

Department

Department of Electrical and Computer Engineering

Abstract

The advent of 5G brought new opportunities to leapfrog beyond current industrial Internet of Things (IoT). However, the ever growing IoT has also attracted adversaries to develop new malware attacks against various IoT applications. Although deep learning-based methods are expected to combat the sophisticated malwares by exploring the latent attack patterns, such detection can be hardly supported by battery-powered end devices, like Andriod-based smartphones. Edge computing enables near-real-time analysis of IoT data by migrating AI-enabled computation-intensive tasks from resource-constrained IoT devices to nearby edge servers, However, due to varying channel conditions and the demanding latency requirements of malware detection, it is challenging to coordinate the computing task offloading among multiple users. By leveraging the computation capacity and the proximity benefits of edge computing, we propose a hierarchical security framework for IoT malware detection. Considering the complexity of AI-enabled malware detection task, we provide a delay-aware computational offloading strategy with minimum delay. Specifically, we construct a coordinated representation learning model, named by Two Stream Attention-Caps (TSA-Caps) to capture the latent behavioral patterns of evolving malware attacks. Experimental results show that our system consistently outperforms the state-of-the-art systems in detection performance on four benchmark datasets.

Publication Title

IEEE Transactions on Industrial Informatics

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