DocumentCode :
669127
Title :
Countering malware evolution using cloud-based learning
Author :
Ouellette, Jacob ; Pfeffer, Avi ; Lakhotia, Arun
fYear :
2013
fDate :
22-24 Oct. 2013
Firstpage :
85
Lastpage :
94
Abstract :
Recent years have seen an explosion in the number and sophistication of malware attacks. The sheer volume of novel malware has made purely manual signature development impractical and has led to research on applying machine learning and data mining to automatically infer malware signatures in the wild. Unfortunately, researchers have recently found ways to game the machine learning algorithms and learn to predict which samples the learning algorithms will classify as benign or malicious, thus opening the door for innovative deception on the part of malware developers. To counter this threat, we are developing our Semi-Supervised Algorithms against Malware Evolution (SESAME) program, which uses online learning to evolve as new malware is encountered, recognizing novel families and adapting its model of families as they themselves evolve. It uses semi-supervised learning to enable it to learn from both labeled and unlabeled malware. SESAME combines a rich feature set with deep learning algorithms to learn the essential characteristics of malware that enable us to relate novel malware to existing malware. SESAME is being designed to be an enterprise-based system with learning in the cloud and rapid endpoint classification.
Keywords :
cloud computing; invasive software; learning (artificial intelligence); pattern classification; SESAME program; cloud-based learning; deep learning algorithms; enterprise-based system; labeled malware; online learning; rapid endpoint classification; semisupervised algorithms against malware evolution; semisupervised learning; unlabeled malware; Classification algorithms; Clustering algorithms; Feature extraction; Machine learning algorithms; Malware; Semantics; Software;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Malicious and Unwanted Software: "The Americas" (MALWARE), 2013 8th International Conference on
Conference_Location :
Fajardo, PR
Print_ISBN :
978-1-4799-2534-6
Type :
conf
DOI :
10.1109/MALWARE.2013.6703689
Filename :
6703689
Link To Document :
بازگشت