@article{(Open Science Index):https://publications.waset.org/pdf/1315, title = {Genetic Programming Approach for Multi-Category Pattern Classification Appliedto Network Intrusions Detection}, author = {K.M. Faraoun and A. Boukelif}, country = {}, institution = {}, abstract = {This paper describes a new approach of classification using genetic programming. The proposed technique consists of genetically coevolving a population of non-linear transformations on the input data to be classified, and map them to a new space with a reduced dimension, in order to get a maximum inter-classes discrimination. The classification of new samples is then performed on the transformed data, and so become much easier. Contrary to the existing GP-classification techniques, the proposed one use a dynamic repartition of the transformed data in separated intervals, the efficacy of a given intervals repartition is handled by the fitness criterion, with a maximum classes discrimination. Experiments were first performed using the Fisher-s Iris dataset, and then, the KDD-99 Cup dataset was used to study the intrusion detection and classification problem. Obtained results demonstrate that the proposed genetic approach outperform the existing GP-classification methods [1],[2] and [3], and give a very accepted results compared to other existing techniques proposed in [4],[5],[6],[7] and [8].}, journal = {International Journal of Computer and Information Engineering}, volume = {1}, number = {10}, year = {2007}, pages = {3111 - 3122}, ee = {https://publications.waset.org/pdf/1315}, url = {https://publications.waset.org/vol/10}, bibsource = {https://publications.waset.org/}, issn = {eISSN: 1307-6892}, publisher = {World Academy of Science, Engineering and Technology}, index = {Open Science Index 10, 2007}, }