Recent worms: a survey and trends
Proceedings of the 2003 ACM workshop on Rapid malcode
ICDCS '04 Proceedings of the 24th International Conference on Distributed Computing Systems (ICDCS'04)
Detecting Kernel-Level Rootkits Through Binary Analysis
ACSAC '04 Proceedings of the 20th Annual Computer Security Applications Conference
Semantics-Aware Malware Detection
SP '05 Proceedings of the 2005 IEEE Symposium on Security and Privacy
Detecting targeted attacks using shadow honeypots
SSYM'05 Proceedings of the 14th conference on USENIX Security Symposium - Volume 14
Spamscatter: characterizing internet scam hosting infrastructure
SS'07 Proceedings of 16th USENIX Security Symposium on USENIX Security Symposium
Proceedings of the 2008 ACM symposium on Applied computing
Detecting malicious code by model checking
DIMVA'05 Proceedings of the Second international conference on Detection of Intrusions and Malware, and Vulnerability Assessment
Identification of potential malicious web pages
AISC '11 Proceedings of the Ninth Australasian Information Security Conference - Volume 116
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Malicious web pages are an increasing threat to current computer systems in recent years. Traditional anti-virus techniques focus typically on detection of the static signatures of Malware and are ineffective against these new threats because they cannot deal with zero-day attacks. In this paper, a novel classification method for detecting malicious web pages is presented. This method is generalization and specialization of attack pattern based on inductive learning, which can be used for updating and expanding knowledge database. The attack pattern is established from an example and generalized by inductive learning, which can be used to detect unknown attacks whose behavior is similar to the example.