Abstract-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). Index Terms-Internet of things malware, internet of battlefield things, malware detection, deep eigenspace learning, deep learning, machine learning
Azmoodeh and Choo REF , used the deep eigenspace learning for malware detection in "Internet Of (Battlefield) Things Devices".
Robust Malware Detection for Internet of (Battlefield) Things Devices Using Deep Eigenspace Learning
IEEE Transactions on Sustainable Computing
IEEE Transactions on Sustainable Computing
Computer Science