Machine Learning
Modeling intrusion detection systems using linear genetic programming approach
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
A linear genetic programming approach to intrusion detection
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A scalable cellular implementation of parallel genetic programming
IEEE Transactions on Evolutionary Computation
Improving network security using genetic algorithm approach
Computers and Electrical Engineering
Review: The use of computational intelligence in intrusion detection systems: A review
Applied Soft Computing
An ensemble-based evolutionary framework for coping with distributed intrusion detection
Genetic Programming and Evolvable Machines
A distributed neural network learning algorithm for network intrusion detection system
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
A distributed hebb neural network for network anomaly detection
ISPA'07 Proceedings of the 5th international conference on Parallel and Distributed Processing and Applications
Evaluation of classification algorithms for intrusion detection in MANETs
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
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In this paper an intrusion detection algorithm based on GP ensembles is proposed. The algorithm runs on a distributed hybrid multi-island model-based environment to monitor security-related activity within a network. Each island contains a cellular genetic program whose aim is to generate a decision-tree predictor, trained on the local data stored in the node. Every genetic program operates cooperatively, yet independently by the others, by taking advantage of the cellular model to exchange the outmost individuals of the population. After the classifiers are computed, they are collected to form the GP ensemble. Experiments on the KDD Cup 1999 Data show the validity of the approach.