Applying Genetic Programming to Evolve Learned Rules for Network Anomaly Detection

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{conf/icnc/YinTHH05,

• title = "Applying Genetic Programming to Evolve Learned Rules for Network Anomaly Detection",
• author = "Chuanhuan Yin and Shengfeng Tian and Houkuan Huang and Jun He",
• year = "2005",
• pages = "323--331",
• editor = "Lipo Wang and Ke Chen and Yew-Soon Ong",
• publisher = "Springer",
• series = "Lecture Notes in Computer Science",
• volume = "3612",
• booktitle = "Advances in Natural Computation, First International Conference, ICNC 2005, Proceedings, Part III",
• address = "Changsha, China",
• month = aug # " 27-29",
• bibdate = "2005-08-01",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icnc/icnc2005-3.html#YinTHH05",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "3-540-28320-X",
• DOI = "doi:10.1007/11539902_38",
• size = "9 pages",
• abstract = "The DARPA/MIT Lincoln Laboratory off-line intrusion detection evaluation data set is the most widely used public benchmark for testing intrusion detection systems. But the presence of simulation artifacts attributes would cause many attacks in this dataset to be easily detected. In order to eliminate their influence on intrusion detection, we simply omit these attributes in the processes of both training and testing. We also present a GP-based rule learning approach for detecting attacks on network. GP is used to evolve new rules from the initial learned rules through genetic operations. Our results show that GP-based rule learning approach outperforms the original rule learning algorithm, detecting 84 of 148 attacks at 100 false alarms despite the absence of several simulation artifacts attributes.",

}

Genetic Programming entries for Chuanyuan Yin Shengfeng Tian Houkuan Huang Jun He

Citations