NDLI: HRS: A Hybrid Framework for Malware Detection

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HRS: A Hybrid Framework for Malware Detection

Content Provider	ACM Digital Library
Author	Xiong, Shuguang Wang, Xin Deng, Xiaolu Feng, Zhentan Huang, Yan Cao, Deqiang Zhou, Xiaobo Wu, Guangzhu Yang, Yang
Abstract	Traditional signature-based detection methods fail to detect unknown malwares, while data mining methods for detection are proved useful to new malwares but suffer for high false positive rate. In this paper, we provide a novel hybrid framework called HRS based on the analysis for 50 millions of malware samples across 20,000 malware classes from our antivirus platform. The distribution of the samples are elaborated and a hybrid framework HRS is proposed, which consists of Hash-based, Rule-based and SVM-based models trained from different classes of malwares according to the distribution. Rule-based model is the core component of the hybrid framework. It is convenient to control false positives by adjusting the factor of a boolean expression in rule-based method, while it still has the ability to detect the unknown malwares. The SVM-based method is enhanced by examining the critical sections of the malwares, which can significantly shorten the scanning and training time. Rigorous experiments have been performed to evaluate the HRS approach based on the massive dataset and the results demonstrate that HRS achieves a true positive rate of 99.84% with an error rate of 0.17%. The HRS method has already been deployed into our security platform.
Starting Page	19
Ending Page	26
Page Count	8
File Format	PDF
ISBN	9781450333412
DOI	10.1145/2713579.2713585
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-03-04
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Malware class distribution Machine learning Antivirus engine Data mining Malware detection
Content Type	Text
Resource Type	Article

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