Digital Threats Research and Practice · 2020 · 56 citations · 11 references
ComputingComputer ScienceCybersecurityInternet Of ThingsMachine LearningMalware AnalysisIot SecurityMalware ClassificationMalware IntelligenceThreat DetectionAccuracyFeature Extraction For Malware AnalysisFeature Extraction For Malware ClassificationIot Malware AnalysisIot Malware DetectionMachine Learning For Malware ClassificationMachine Learning-based Malware DetectionMalware Feature Extraction
Threats to devices that are part of the Internet of Things (IoT) are on the rise. Owing to the overwhelming diversity of IoT hardware and software, as well as its variants, conventional anti-virus techniques based on the Windows paradigm cannot be applied directly to counter threats to the IoT devices. In this article, we propose a framework that can efficiently analyze IoT malware in a wide range of environments. It consists of a universal feature representation obtained by static analysis of the malware and a machine learning scheme that first detects the malware and then classifies it into a known category. The framework was evaluated by applying it to a recently developed dataset consisting of more than 6,000 IoT malware samples collected from the HoneyPot project. The results show that the proposed method can obtain near-optimal accuracy in terms of the detection and classification of malware targeting IoT devices.
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Proceedings of the 2017 USENIX Annual Technical Conference (USENIX ATC ’17)
Do Le Quoc, Martin S. Beck, Pramod Bhatotia et al. · 2017 · 611 citations
Malware Analysis and Classification: A Survey
Ekta Gandotra, Divya Bansal, Sanjeev Sofat · Journal of Information Security · 2014 · 409 citations · Full text