most citedUnsupervised Anomaly-based Malware Detection using Hardware Features

15 citations · 15 across the 1 of their papers we have counts for

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1 paper

cs.CR2014★ 15 cited

Unsupervised Anomaly-based Malware Detection using Hardware Features

Adrian Tang, Simha Sethumadhavan, Salvatore Stolfo

Recent works have shown promise in using microarchitectural execution patterns to detect malware programs. These detectors belong to a class of detectors known as signature-based d…