Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A Sense of Self for Unix Processes
SP '96 Proceedings of the 1996 IEEE Symposium on Security and Privacy
Operating system stability and security through process homeostasis
Operating system stability and security through process homeostasis
On evolving buffer overflow attacks using genetic programming
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Undermining an anomaly-based intrusion detection system using common exploits
RAID'02 Proceedings of the 5th international conference on Recent advances in intrusion detection
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The testing of anomaly detectors is considered from the perspective of a Multi-objective Evolutionary Exploit Generator (EEG). Such a framework provides users of anomaly detection systems two capabilities. Firstly, no knowledge of protected data structures need be assumed. Secondly, the evolved exploits are then able to demonstrate weaknesses in the ensuing detector parameterization. In this work we focus on the parameterization of the second generation anomaly detector `pH' and demonstrate how use of an EEG may identify weak parameterization of the detector.