Optimizing Malware Detection for IoT and Edge Environments With Quantization Awareness

Abstract

Despite the widespread applicability of malware detection on the Internet of Things (IoT) and edge platforms, efficient malware detection on edge devices is significantly challenging due to hardware limitations. This study proposes a novel edge-friendly framework for learning malware detection by incorporating quantization awareness. The proposed framework incorporates a lightweight deep neural network leveraging long-distance pixel dependency, spatial-asymmetric attention, and normalization-free multi-head attention (MHA) block to refine salient features. Additionally, the proposed framework introduces an efficient quantization-aware training (QAT) strategy to optimize the proposed model for edge-friendly INT8 precision. The practicality of the proposed model has been validated through deployment on ARM-based hardware and extensive evaluations on standard x64 hardware. Experimental results show that the proposed method significantly outperforms existing malware detection methods in numerous precision modes on both platforms. The proposed method achieved 99.36% accuracy on the MalImg dataset and 98.41% on the IoT malware dataset with FP16 precision at 662 fps. With INT8 precision, it achieved 95.62% accuracy on the MalImg dataset and 97.36% on the IoT malware dataset, maintaining 1197 fps on a low-power edge device. To our knowledge, this is the first work in the open literature demonstrating QAT’s practicality for malware detection on edge platforms.

Keywords

• Quantization aware training
• malware classification
• deep learning
• attention
• edge device