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

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 detec...

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Bibliographic Details
Published in:Genetic programming and evolvable machines 2010-06, Vol.11 (2), p.131-146
Main Authors: Folino, Gianluigi, Pizzuti, Clara, Spezzano, Giandomenico
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Biomedical Engineering and Bioengineering
Classification
Compilers
Computer Science
Construction
Dealing
Electrical Engineering
Genetics
Interpreters
Intrusion
Networks
Original Paper
Programming
Programming Languages
Programming Techniques
Software Engineering/Programming and Operating Systems
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Summary: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.
ISSN:1389-2576
1573-7632
DOI:10.1007/s10710-010-9101-6