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

Authors:
Gianluigi Folino;Clara Pizzuti;Giandomenico Spezzano
Affiliations:
National Research Council (CNR), Institute for High Performance Computing and Networking (ICAR), Rende, Italy 87036;National Research Council (CNR), Institute for High Performance Computing and Networking (ICAR), Rende, Italy 87036;National Research Council (CNR), Institute for High Performance Computing and Networking (ICAR), Rende, Italy 87036
Venue:
Genetic Programming and Evolvable Machines
Year:
2010

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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.