%0 Conference Paper %1 song:2003:gecco %A Song, Dong %A Heywood, Malcolm I. %A Zincir-Heywood, A. Nur %B Genetic and Evolutionary Computation -- GECCO-2003 %C Chicago %D 2003 %E Cantú-Paz, E. %E Foster, J. A. %E Deb, K. %E Davis, D. %E Roy, R. %E O'Reilly, U.-M. %E Beyer, H.-G. %E Standish, R. %E Kendall, G. %E Wilson, S. %E Harman, M. %E Wegener, J. %E Dasgupta, D. %E Potter, M. A. %E Schultz, A. C. %E Dowsland, K. %E Jonoska, N. %E Miller, J. %I Springer-Verlag %K Applications Real World algorithms, genetic programming, %P 2325--2336 %T A Linear Genetic Programming Approach to Intrusion Detection %U http://users.cs.dal.ca/~mheywood/X-files/Publications/27242325.pdf %V 2724 %X Page-based Linear Genetic Programming (GP) is proposed and implemented with two-layer Subset Selection to address a two-class intrusion detection classification problem as defined by the KDD-99 benchmark dataset. By careful adjustment of the relationship between subset layers, over fitting by individuals to specific subsets is avoided. Moreover, efficient training on a dataset of 500,000 patterns is demonstrated. Unlike the current approaches to this benchmark, the learning algorithm is also responsible for deriving useful temporal features. Following evolution, decoding of a GP individual demonstrates that the solution is unique and comparative to hand coded solutions found by experts. %@ 3-540-40603-4

@inproceedings{song:2003:gecco, abstract = {Page-based Linear Genetic Programming (GP) is proposed and implemented with two-layer Subset Selection to address a two-class intrusion detection classification problem as defined by the KDD-99 benchmark dataset. By careful adjustment of the relationship between subset layers, over fitting by individuals to specific subsets is avoided. Moreover, efficient training on a dataset of 500,000 patterns is demonstrated. Unlike the current approaches to this benchmark, the learning algorithm is also responsible for deriving useful temporal features. Following evolution, decoding of a GP individual demonstrates that the solution is unique and comparative to hand coded solutions found by experts.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Chicago}, author = {Song, Dong and Heywood, Malcolm I. and Zincir-Heywood, A. Nur}, biburl = {https://www.bibsonomy.org/bibtex/2f6af16a8b81413912896c924eac774d5/brazovayeye}, booktitle = {Genetic and Evolutionary Computation -- GECCO-2003}, editor = {Cant{\'u}-Paz, E. and Foster, J. A. and Deb, K. and Davis, D. and Roy, R. and O'Reilly, U.-M. and Beyer, H.-G. and Standish, R. and Kendall, G. and Wilson, S. and Harman, M. and Wegener, J. and Dasgupta, D. and Potter, M. A. and Schultz, A. C. and Dowsland, K. and Jonoska, N. and Miller, J.}, interhash = {19117b556fae0d93a477f020ce4ba6f7}, intrahash = {f6af16a8b81413912896c924eac774d5}, isbn = {3-540-40603-4}, keywords = {Applications Real World algorithms, genetic programming,}, month = {12-16 July}, notes = {GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)}, pages = {2325--2336}, publisher = {Springer-Verlag}, publisher_address = {Berlin}, series = {LNCS}, timestamp = {2008-06-19T17:51:56.000+0200}, title = {A Linear Genetic Programming Approach to Intrusion Detection}, url = {http://users.cs.dal.ca/~mheywood/X-files/Publications/27242325.pdf}, volume = 2724, year = 2003 }