Malware Family Classification using Active Learning by Learning
Chin-Wei Chen, Ching-Hung Su, Kun-Wei Lee, Ping-Hao Bair
Abstract
In the past few years, the malware industry has been thriving. Malware variants among the same malware family shared similar behavioural patterns or signatures reflecting their purpose. We propose an approach that combines support vector machine (SVM) classifiers and active learning by learning (ALBL) techniques to deal with insufficient labeled data in terms of the malware classification tasks. The proposed approach is evaluated with the malware family dataset from Microsoft Malware Classification Challenge (BIG 2015) on Kaggle. The results show that ALBL techniques can effectively boost the performance of our machine learning models and improve the quality of labeled samples.
Topics & Concepts
MalwareComputer scienceMachine learningArtificial intelligenceSupport vector machineThrivingMalware analysisComputer securitySocial scienceSociologyAdvanced Malware Detection TechniquesNetwork Security and Intrusion DetectionAnomaly Detection Techniques and Applications