Towards intrusion detection by information retrieval and genetic programming
Pavel Krömer, Jan Platoš, Václav Snåšel, Ajith Abraham · 2010
Fuzzy classifiers and fuzzy rules are powerful tools in data mining and knowledge discovery. In this work, intrusion detection is approached as a data mining task and genetic programming is deployed to evolve fuzzy classifiers for detection of intrusion and security problems. We train the fuzzy classifier on a data set modeled as a fuzzy information retrieval collection and investigate its ability to detect illegitimate actions. Proposed approach is experimentally evaluated on the popular KDD Cup intrusion detection data set.