An ensemble-based evolutionary framework for coping with distributed intrusion detection

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{Folino:2010:GPEM,

• author = "Gianluigi Folino and Clara Pizzuti and Giandomenico Spezzano",
• title = "An ensemble-based evolutionary framework for coping with distributed intrusion detection",
• journal = "Genetic Programming and Evolvable Machines",
• year = "2010",
• volume = "11",
• number = "2",
• pages = "131--146",
• month = jun,
• note = "Special issue on parallel and distributed evolutionary algorithms, part II",
• keywords = "genetic algorithms, genetic programming, Intrusion detection, Ensemble classifiers, Distributed evolutionary algorithms",
• ISSN = "1389-2576",
• DOI = "doi:10.1007/s10710-010-9101-6",
• size = "16 pages",
• abstract = "A distributed data mining algorithm to improve the detection accuracy when classifying malicious or unauthorized network activity is presented. The algorithm is based on genetic programming (GP) extended with the ensemble paradigm. GP ensemble is particularly suitable for distributed intrusion detection because it allows to build a network profile by combining different classifiers that together provide complementary information. The main novelty of the algorithm is that data is distributed across multiple autonomous sites and the learner component acquires useful knowledge from this data in a cooperative way. The network profile is then used to predict abnormal behavior. Experiments on the KDD Cup 1999 Data show the capability of genetic programming in successfully dealing with the problem of intrusion detection on distributed data.",

}

Genetic Programming entries for Gianluigi Folino Clara Pizzuti Giandomenico Spezzano

Citations