Robust Malware Detection for Internet of (Battlefield) Things Devices Using Deep Eigenspace Learning

• Published in: IEEE transactions on sustainable computing 2019-01, Vol.4 (1), p.88-95
• Main Authors: Azmoodeh, Amin, Dehghantanha, Ali, Choo, Kim-Kwang Raymond
• Format: Article
• Language: English
• Subjects: Androids; Battlefields; Computer security; deep eigenspace learning; deep learning; Feature extraction; Internet; internet of battlefield things; Internet of Things; Internet of things malware; Machine learning; Malware; malware detection; Medical devices; Medical electronics; Medical equipment; Military technology; Nodes; Robustness
• ISSN: 2377-3782; 2377-3782; 2377-3790
• DOI: 10.1109/TSUSC.2018.2809665

Internet of Things (IoT) in military settings generally consists of a diverse range of Internet-connected devices and nodes (e.g., medical devices and wearable combat uniforms). These IoT devices and nodes are a valuable target for cyber criminals, particularly state-sponsored or nation state actors. A common attack vector is the use of malware. In this paper, we present a deep learning based method to detect Internet Of Battlefield Things (IoBT) malware via the device's Operational Code (OpCode) sequence. We transmute OpCodes into a vector space and apply a deep Eigenspace learning approach to classify malicious and benign applications. We also demonstrate the robustness of our proposed approach in malware detection and its sustainability against junk code insertion attacks. Lastly, we make available our malware sample on Github, which hopefully will benefit future research efforts (e.g., to facilitate evaluation of future malware detection approaches).