Evaluating the performance of a differential evolution algorithm in anomaly detection
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- @InProceedings{Elsayed:2015:CEC,
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author = "Saber Elsayed and Ruhul Sarker and Jill Slay",
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booktitle = "2015 IEEE Congress on Evolutionary Computation (CEC)",
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title = "Evaluating the performance of a differential evolution
algorithm in anomaly detection",
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year = "2015",
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pages = "2490--2497",
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isbn13 = "978-1-4799-7491-7",
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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.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/CEC.2015.7257194",
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ISSN = "1089-778X",
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month = may,
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notes = "Also known as \cite{7257194}",
- }
Genetic Programming entries for
Saber Elsayed
Ruhul Sarker
Jill Slay
Citations