Dynamic Training Subset Selection for Supervised Learning in Genetic Programming
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Weaknesses in the Key Scheduling Algorithm of RC4
SAC '01 Revised Papers from the 8th Annual International Workshop on Selected Areas in Cryptography
An Application of Machine Learning to Network Intrusion Detection
ACSAC '99 Proceedings of the 15th Annual Computer Security Applications Conference
A Comparative Study of Techniques for Intrusion Detection
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
A Software Implementation of a Genetic Algorithm Based Approach to Network Intrusion Detection
SNPD-SAWN '05 Proceedings of the Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Networks
802.11 denial-of-service attacks: real vulnerabilities and practical solutions
SSYM'03 Proceedings of the 12th conference on USENIX Security Symposium - Volume 12
Training genetic programming on half a million patterns: an example from anomaly detection
IEEE Transactions on Evolutionary Computation
Dynamic page based crossover in linear genetic programming
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Defence against 802.11 dos attacks using artificial immune system
ICARIS'07 Proceedings of the 6th international conference on Artificial immune systems
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This paper presents a genetic programming approach to detect deauthentication attacks on wireless networks based on the 802.11 protocol. To do so we focus on developing an appropriate fitness function and feature set. Results show that the intrusion system developed not only performs incredibly well – 100 percent detection rate and 0.5 percent false positive rate – but also developed a solution that is general enough to detect similar attacks, such as disassociation attacks, that were not present in the training data.