You are in:Home/Publications/Genetic Algorithm with Different Feature Selection Techniques for Anomaly Detectors Generation. Federated Conference On Computer Science And Information Systems Kraków, Poland, 8 - 11 September, 2013.

Dr. Assoc. Prof. Ahmad Taher Azar :: Publications:

Title:
Genetic Algorithm with Different Feature Selection Techniques for Anomaly Detectors Generation. Federated Conference On Computer Science And Information Systems Kraków, Poland, 8 - 11 September, 2013.
Authors: Amira Sayed A. Aziz, Aboul Ella Hassanien, Ahmad Taher Azar, Sanaa El-Ola Hanafy
Year: 2013
Keywords: Not Available
Journal: Not Available
Volume: Not Available
Issue: Not Available
Pages: Not Available
Publisher: Not Available
Local/International: International
Paper Link: Not Available
Full paper Ahmad Taher Azar_106.pdf
Supplementary materials Not Available
Abstract:

Intrusion detection systems have been around for quite some time, to protect systems from inside ad outside threats. Researchers and scientists are concerned on how to enhance the intrusion detection performance, to be able to deal with real-time attacks and detect them fast from quick response. One way to improve performance is to use minimal number of features to de?ne a model in a way that it can be used to accurately discriminate normal from anomalous behaviour. Many feature selection techniques are out there to reduce feature sets or extract new features out of them. In this paper, we propose an anomaly detectors generation approach using genetic algorithm in conjunction with several features selection techniques, including principle components analysis, sequential ?oating, and correlation-based feature selection. A Genetic algorithm was applied with deterministic crowding niching technique, to generate a set of detectors from a single run. The results show that sequential-?oating techniques with the genetic algorithm have the best results, compared to others tested, especially the sequential ?oating forward selection with detection accuracy 92.86% on the train set and 85.38% on the test set.

Google ScholarAcdemia.eduResearch GateLinkedinFacebookTwitterGoogle PlusYoutubeWordpressInstagramMendeleyZoteroEvernoteORCIDScopus