000000278 001__ 278
000000278 005__ 20130522141957.0
000000278 037__ $$aLERSSE-POSTER-2012-001
000000278 100__ $$aYazan Boshmaf
000000278 245__ $$aAugur: Aiding Malware Detection Using Large-Scale Machine Learning
000000278 260__ $$c2012-08-05
000000278 300__ $$amult. p
000000278 520__ $$aWe present Augur: a large-scale machine learning system that uses malware static and dynamic analyses to predict the maliciousness of new files. Unlike other machine learning-based malware detection systems, Augur utilizes existing knowledge engineering performed by analysts and uses static and dynamic file properties (called Genes and Phenoms, respectively) as prominent predictive features. Augur can be deployed along side existing detection systems (e.g., an expert system) in order to achieve faster reactions to suspicious files at the endpoint, and to automatically generate effective signatures of new, unseen before malware.
000000278 6531_ $$aMalware Detection
000000278 6531_ $$aMachine Learning
000000278 700__ $$aMatei Ripeanu
000000278 700__ $$aKonstantin Beznosov
000000278 700__ $$aKyle Zeeuwen
000000278 700__ $$aDavid Cornell
000000278 700__ $$aDmitry Samosseiko
000000278 8560_ $$fboshmaf@ece.ubc.ca
000000278 8564_ $$uhttp://lersse-dl.ece.ubc.ca/record/278/files/278.pdf$$yTransfer from CDS 0.99.7
000000278 909C4 $$pYazan Boshmaf, Matei Ripeanu, Konstantin Beznosov, Kyle Zeeuwen, David Cornell, Dmitry Samosseiko. Augur: Aiding Malware Detection Using Large-Scale Machine Learning. At the Poster Session of the 21st Usenix Security Symposium, Bellevue, WA, 2012
000000278 980__ $$aPOSTER