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Edge-Based IIoT Malware Detection for Mobile Devices With Offloading.pdf
1. Edge-Based IIoT Malware Detection for
Mobile Devices With Offloading
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, such as
Android-based smartphones. Edge computing enables the near-real-time
analysis of IoT data by migrating artificial intelligence (AI)-enabled
computation-intensive tasks from resource-constrained IoT devices to nearby
edge servers. However, owing 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.
2. Considering the complexity of the 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, 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.