Performance Evaluation of a Genetic Algorithm Based Approach to Network Intrusion Detection System
Abdullah, B., Abd-alghafar, I., Salama, G., Abd-alhafez, A. (2009). Performance Evaluation of a Genetic Algorithm Based Approach to Network Intrusion Detection System. EKB Journal Management System, 13(AEROSPACE SCIENCES & AVIATION TECHNOLOGY, ASAT- 13, May 26 – 28, 2009), 1-17. doi: 10.21608/asat.2009.23490
B. Abdullah; I. Abd-alghafar; Gouda I. Salama; A. Abd-alhafez. "Performance Evaluation of a Genetic Algorithm Based Approach to Network Intrusion Detection System". EKB Journal Management System, 13, AEROSPACE SCIENCES & AVIATION TECHNOLOGY, ASAT- 13, May 26 – 28, 2009, 2009, 1-17. doi: 10.21608/asat.2009.23490
Abdullah, B., Abd-alghafar, I., Salama, G., Abd-alhafez, A. (2009). 'Performance Evaluation of a Genetic Algorithm Based Approach to Network Intrusion Detection System', EKB Journal Management System, 13(AEROSPACE SCIENCES & AVIATION TECHNOLOGY, ASAT- 13, May 26 – 28, 2009), pp. 1-17. doi: 10.21608/asat.2009.23490
Abdullah, B., Abd-alghafar, I., Salama, G., Abd-alhafez, A. Performance Evaluation of a Genetic Algorithm Based Approach to Network Intrusion Detection System. EKB Journal Management System, 2009; 13(AEROSPACE SCIENCES & AVIATION TECHNOLOGY, ASAT- 13, May 26 – 28, 2009): 1-17. doi: 10.21608/asat.2009.23490

Performance Evaluation of a Genetic Algorithm Based Approach to Network Intrusion Detection System

International Conference on Aerospace Sciences and Aviation Technology
Article 17, Volume 13, AEROSPACE SCIENCES & AVIATION TECHNOLOGY, ASAT- 13, May 26 – 28, 2009, May 2009, Page 1-17  XML PDF (267.98 K)
Document Type: Original Article
DOI: 10.21608/asat.2009.23490
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Authors
B. Abdullah1; I. Abd-alghafar2; Gouda I. Salama2; A. Abd-alhafez2
1Yemeni Armed Forces.
2Egyptian Armed Forces.
Abstract
The purpose of the work described in this paper is to provide an intrusion detection system (IDS), by applying genetic algorithm (GA) to network intrusion detection system. Parameters and evolution process for GA are discussed in detail and implemented. This approach uses information theory to filter the traffic data and thus reduce the complexity. We use a linear structure rule to classify the network behaviors into normal and abnormal behaviors. This approach applied to the KDD99 benchmark dataset and obtained high detection rate up to 99.87% as well as low false positive rate 0.003%. Finally the results of this approach compared with available machine learning techniques.
Keywords
Intrusion Detection System; Genetic Algorithm; Open Source Weka software
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