Semi-Supervised Learning for Unknown Malware Detection

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Provided by: University of Detroit Mercy
Topic: Security
Format: PDF
Malware is any kind of computer software potentially harmful to both computers and networks. The amount of malware is increasing every year and poses a serious global security threat. Signature-based detection is the most widely used commercial antivirus method, however, it consistently fails to detect new malware. In this paper, the authors propose a new method of malware protection that adopts a semi-supervised learning approach to detect unknown malware. This method is designed to build a machine-learning classifier using a set of labelled (malware and legitimate software) and unlabelled instances. They performed an empirical validation demonstrating that the labelling efforts are lower than when supervised learning is used, while maintaining high accuracy rates.
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