A Sense of Self for Unix Processes
SP '96 Proceedings of the 1996 IEEE Symposium on Security and Privacy
A multi-model approach to the detection of web-based attacks
Computer Networks: The International Journal of Computer and Telecommunications Networking - Web security
Learning DFA representations of HTTP for protecting web applications
Computer Networks: The International Journal of Computer and Telecommunications Networking
USENIX-SS'06 Proceedings of the 15th conference on USENIX Security Symposium - Volume 15
Comparing anomaly detection techniques for HTTP
RAID'07 Proceedings of the 10th international conference on Recent advances in intrusion detection
Model generalization and its implications on intrusion detection
ACNS'05 Proceedings of the Third international conference on Applied Cryptography and Network Security
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Anomaly detection systems have the potential to detect zero-day attacks. However, these systems can suffer from high rates of false positives and can be evaded through through mimicry attacks. The key to addressing both problems is careful control of model generalization. An anomaly detection system that undergeneralizes generates too many false positives, while one that overgeneralizes misses attacks. In this paper, we present a methodology for creating anomaly detection systems that make appropriate trade-offs regarding model precision and generalization. Specifically, we propose that systems be created by taking an appropriate, undergeneralizing data modeling method and extending it using data pre-processing generalization heuristics. To show the utility of our methodology, we show how it has been applied to the problem of detecting malicious web requests.