Detecting new forms of network intrusion using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{lu:2003:dnfoniugp,

• author = "Wei Lu and Issa Traore",
• title = "Detecting new forms of network intrusion using genetic programming",
• booktitle = "Proceedings of the 2003 Congress on Evolutionary Computation CEC2003",
• editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon",
• pages = "2165--2172",
• year = "2003",
• publisher = "IEEE Press",
• address = "Canberra",
• publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA",
• month = "8-12 " # dec,
• organisation = "IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)",
• keywords = "genetic algorithms, genetic programming, Artificial intelligence, Biological cells, Data structures, Databases, Event detection, Genetic mutations, Intrusion detection, Testing, authorisation, telecommunication security, DARPA, crossover, detection rate, dropping condition operators, false alarm rate, false negative rate, false positive rate, genetic operators, intrusion detection systems, mutation, network attacks, network intrusion, reproduction, rule evolution approach, testing dataset, training dataset,",
• URL = "http://www.isot.ece.uvic.ca/publications/journals/coi-2004.pdf",
• DOI = "doi:10.1109/CEC.2003.1299940",
• ISBN = "0-7803-7804-0",
• abstract = "How to find and detect novel or unknown network attacks is one of the most important objectives in current intrusion detection systems. In this paper, a rule evolution approach based on Genetic Programming (GP) for detecting novel attacks on network is presented and four genetic operators namely reproduction, mutation, crossover and dropping condition operators are used to evolve new rules. New rules are used to detect novel or known network attacks. A training and testing dataset proposed by DARPA is used to evolve and evaluate these new rules. The proof of concept implementation shows that the rule generated by GP has a low false positive rate (FPR), a low false negative rate (FNR) and a high rate of detecting unknown attacks. Moreover, the rule base composed of new rules has high detection rate (DR) with low false alarm rate (FAR).",
• notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.",

}

Genetic Programming entries for Wei Lu Issa Traore

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