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- W1507431306 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%." @default.
- W1507431306 created "2016-06-24" @default.
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- W1507431306 date "2015-05-01" @default.
- W1507431306 modified "2023-09-26" @default.
- W1507431306 title "Evaluating the performance of a differential evolution algorithm in anomaly detection" @default.
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- W1507431306 doi "https://doi.org/10.1109/cec.2015.7257194" @default.
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