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

Citations