Evaluating the performance of a differential evolution algorithm in anomaly detection

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@InProceedings{Elsayed:2015:CEC,

• author = "Saber Elsayed and Ruhul Sarker and Jill Slay",
• booktitle = "2015 IEEE Congress on Evolutionary Computation (CEC)",
• title = "Evaluating the performance of a differential evolution algorithm in anomaly detection",
• year = "2015",
• pages = "2490--2497",
• 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 optimisation 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 10percentKDD99 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 100percent, while the average false positive rate is only 0.582percent.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/CEC.2015.7257194",
• ISSN = "1089-778X",
• month = may,
• notes = "Also known as \cite{7257194}",

}

Genetic Programming entries for Saber Elsayed Ruhul Sarker Jill Slay

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