2020
DOI: 10.1109/access.2019.2962198
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Semi-Supervised Malware Clustering Based on the Weight of Bytecode and API

Abstract: With the rapid advances of anti-virus and anti-tracking technologies, three aspects in malware clustering need to be improved for effective clustering, i.e., the robustness of features, the accuracy of similarity measurements, and the effectiveness of clustering algorithms. In this paper, we propose a novel malware family clustering approach based on dynamic and static features with their weights. In this approach, we employ a new similarity measurement method based on EMD to improve the accuracy of feature si… Show more

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Cited by 10 publications
(1 citation statement)
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“…Representation learning focuses on finding the features that maximize the performance of a ML classifier [78]. It is possible to use unlabelled data to finetune such selection (e.g., [79]- [83]). Such procedures are ancillary to detection tasks, and hence outside our scope.…”
Section: Related Workmentioning
confidence: 99%
“…Representation learning focuses on finding the features that maximize the performance of a ML classifier [78]. It is possible to use unlabelled data to finetune such selection (e.g., [79]- [83]). Such procedures are ancillary to detection tasks, and hence outside our scope.…”
Section: Related Workmentioning
confidence: 99%