Machine Learning
Intrusion Detection Using Variable-Length Audit Trail Patterns
RAID '00 Proceedings of the Third International Workshop on Recent Advances in Intrusion Detection
A Sense of Self for Unix Processes
SP '96 Proceedings of the 1996 IEEE Symposium on Security and Privacy
Semantics-Aware Malware Detection
SP '05 Proceedings of the 2005 IEEE Symposium on Security and Privacy
Data mining approaches for intrusion detection
SSYM'98 Proceedings of the 7th conference on USENIX Security Symposium - Volume 7
Learning and Classification of Malware Behavior
DIMVA '08 Proceedings of the 5th international conference on Detection of Intrusions and Malware, and Vulnerability Assessment
Proceedings of the 2nd ACM workshop on Security and artificial intelligence
Evolutionary neural networks for anomaly detection based on the behavior of a program
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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One of the major problems concerning information assurance is malicious code. In order to detect them, many existing run-time intrusion or malware detection techniques utilize information available in Application Programming Interface (API) call sequences to discriminate between benign and malicious processes. Although some great progresses have been made, the new research results of ensemble learning make it possible to design better malware detection algorithm. This paper present a novel approach of detecting malwares using API call sequences. Basing on the fact that the API call sequences of a software show local property when doing network, file IO and other operations, we first divide the API call sequences of a malware into seven subsequences, and then use each subsequence to build a classification model. After these building models are used to classify software, their outputs are combined by using BKS and the final fusion results will be used to label whether a software is malicious or not. Experiments show that our algorithm can detect known malware effectively.