2009 IntelligentFileScoringSystemfor

• (Ye et al., 2009) ⇒ Yanfang Ye, Tao Li, Qingshan Jiang, Zhixue Han, and Li Wan. (2009). “Intelligent File Scoring System for Malware Detection from the Gray List.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557167

Subject Headings:

Notes

• Categories and Subject Descriptors: I.2.6 Artificial Intelligence: Learning; D.4.6 Operating System: Security and Protection - Invasive software.
• General Terms: Algorithms, Experimentation, Security

Cited By

• http://scholar.google.com/scholar?q=%22Intelligent+file+scoring+system+for+malware+detection+from+the+gray+list%22+2009
• http://portal.acm.org/citation.cfm?doid=1557019.1557167&preflayout=flat#citedby

Quotes

Author Keywords

Malware Detection, Gray List, Ensemble

Abstract

Currently, the most significant line of defense against malware is anti-virus products which focus on authenticating valid software from a white list, blocking invalid software from a black list, and running any unknown software (i.e., the gray list) in a controlled manner. The gray list, containing unknown software programs which could be either normal or malicious, is usually authenticated or rejected manually by virus analysts. Unfortunately, along with the development of the malware writing techniques, the number of file samples in the gray list that need to be analyzed by virus analysts on a daily basis is constantly increasing. In this paper, we develop an intelligent file scoring system (IFSS for short) for malware detection from the gray list by an ensemble of heterogeneous base-level classifiers derived by different learning methods, using different feature representations on dynamic training sets. To the best of our knowledge, this is the first work of applying such ensemble methods for malware detection. IFSS makes it practical for virus analysts to identify malware samples from the huge gray list and improves the detection ability of anti-virus software. It has already been incorporated into the scanning tool of Kingsoft's Anti-Virus software. The case studies on large and real daily collection of the gray list illustrate that the detection ability and efficiency of our IFSS system outperforms other popular scanning tools such as NOD32 and Kaspersky.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 IntelligentFileScoringSystemfor	Yanfang Ye Tao Li Qingshan Jiang Zhixue Han Li Wan			Intelligent File Scoring System for Malware Detection from the Gray List		KDD-2009 Proceedings		10.1145/1557019.1557167		2009