On evolving buffer overflow attacks using genetic programming
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence (Studies in Computational Intelligence)
Snort Intrusion Detection and Prevention Toolkit
Snort Intrusion Detection and Prevention Toolkit
Survey of network-based defense mechanisms countering the DoS and DDoS problems
ACM Computing Surveys (CSUR)
Denial of service detection and analysis using idiotypic networks paradigm
Proceedings of the 10th annual conference on Genetic and evolutionary computation
An immunity-based technique to characterize intrusions in computernetworks
IEEE Transactions on Evolutionary Computation
Applied Computational Intelligence and Soft Computing - Special issue on theory and applications of evolutionary computation
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The paper proposes a multiobjective approach to the problem of malicious network traffic classification, with specificity and sensitivity criteria as objective functions for the problem. The multiobjective version of Gene Expression Programming (GEP) called moGEP is proposed and applied to find proper classifiers in the multiobjective search space. The purpose of the classifiers is to discriminate information about the network traffic obtained from Idiotypic Network-based Intrusion Detection System (INIDS), transformed into time series. The proposed approach is validated using the network traffic simulator ns2. Classifiers of high accuracy are obtained and their diversity offers interesting possibilities to the domain of network security.