802.11 denial-of-service attacks: real vulnerabilities and practical solutions
SSYM'03 Proceedings of the 12th conference on USENIX Security Symposium - Volume 12
A linear genetic programming approach to intrusion detection
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
A solution to spoofed PS-poll based denial of service attacks in IEEE 802.11 WLANs
ICCOM'07 Proceedings of the 11th Conference on 11th WSEAS International Conference on Communications - Volume 11
WSEAS TRANSACTIONS on COMMUNICATIONS
Review: The use of computational intelligence in intrusion detection systems: A review
Applied Soft Computing
honeyM: a framework for implementing virtual honeyclients for mobile devices
Proceedings of the third ACM conference on Wireless network security
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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Genetic Programming (GP) based Intrusion Detection Systems (IDS) use connection state network data during their training phase. These connection states are recorded as a set of features that the GP uses to train and test solutions which allow for the efficient and accurate detection of given attack patterns. However, when applied to a 802.11 network that is faced with attacks specific to the 802.11 protocol, the GP's detection rate reduces dramatically. In this work we discuss what causes this effect, and what can be done to improve the GP's performance on 802.11 networks.