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

By Amin Azmoodeh, Ali Dehghantanha, Kim-Kwang Raymond Choo. IEEE Transactions on Sustainable Computing, 2019

307 citations recorded by the source corpus.

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).

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/777db22856fd23f7ce778d28c7cf258d25a61b42

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Citations in the corpus

An Ensemble Intrusion Detection Technique Based on Proposed Statistical Flow Features for Protecting Network Traffic of Internet of Things (2019, methodology): “[18] developed a deep learning technique to identify malware of Internet of Battlefield Things by analyzing”
Demystifying IoT Security: An Exhaustive Survey on IoT Vulnerabilities and a First Empirical Look on Internet-Scale IoT Exploitations (2019, background): “IoT security [204]–[207], deep learning methodologies for inferring and characterizing IoT maliciousness [208], [209], and the adoption of SDN and cloud paradigms for IoT resiliency [210]–[212].”
Applications of Wireless Sensor Networks and Internet of Things Frameworks in the Industry Revolution 4.0: A Systematic Literature Review (2022, methodology): “Deep learning [46]algorithms such as CNN, auto-encoders, and recurrent neural networks have applications such as intelligent assembly and manufacturing, network monitoring, and accident prevention.”