Evaluating the performance of a differential evolution algorithm in anomaly detection
Date
2015
Authors
Elsayed, S.
Sarker, R.
Slay, J.
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Conference paper
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2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings, 2015, iss.7257194, pp.2490-2497
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IEEE Congress on Evolutionary Computation, CEC 2015 (25 May 2015 - 28 May 2015 : Sendai, Japan)
Abstract
During the last few eras, evolutionary algorithms have been adopted to tackle cyber-terrorism. Among them, genetic algorithms and genetic programming were popular choices. Recently, it has been shown that differential evolution was more successful in solving a wide range of optimization problems. However, a very limited number of research studies have been conducted for intrusion detection using differential evolution. In this paper, we will adapt differential evolution algorithm for anomaly detection, along with proposing a new fitness function to measure the quality of each individual in the population. The proposed method is trained and tested on the 10%KDD99 cup data and compared against existing methodologies. The results show the effectiveness of using differential evolution in detecting anomalies by achieving an average true positive rate of 100%, while the average false positive rate is only 0.582%.
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Copyright 2015 IEEE