Lightweight IoT Malware Visualization Analysis via Two-Bits Networks

Internet of Things (IoT) devices are typically resource constrained micro-computers for domain-specific computations. Most of them use low-cost embedded system that lacked basic security monitoring and protection mechanisms. Consequently, IoT-specific malwares are made to target at these vulnerable devices for deep infection and utilization, such as Mirai and Brickerbot, which poses tremendous threats to the security of IoT. In this issue, we present a novel approach for detecting malware in IoT environments. The proposed method firstly extract one-channel gray-scale image sequence that converted from the disassembled malware binaries. Then we utilize a Two-Bits Convolutional Neural Network (TBN) for detecting IoT malware families, which can encode the network edge weights with two bits. Experimental results conducted on the collected dataset show that our approach can reduce the memory usage and improve computational efficiency significantly while achieving a considerable performance in terms of malware detection accuracy.