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DOI: 10.14569/IJACSA.2017.080461
PDF

DoS Detection Method based on Artificial Neural Networks

Author 1: Mohamed Idhammad
Author 2: Karim Afdel
Author 3: Mustapha Belouch

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 8 Issue 4, 2017.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: DoS attack tools have become increasingly sophis-ticated challenging the existing detection systems to continually improve their performances. In this paper we present a victim-end DoS detection method based on Artificial Neural Networks (ANN). In the proposed method a Feed-forward Neural Network (FNN) is optimized to accurately detect DoS attack with minimum resources usage. The proposed method consists of the following three major steps: (1) Collection of the incoming network traffic,(2) selection of relevant features for DoS detection using an unsupervised Correlation-based Feature Selection (CFS) method,(3) classification of the incoming network traffic into DoS traffic or normal traffic. Various experiments were conducted to evaluate the performance of the proposed method using two public datasets namely UNSW-NB15 and NSL-KDD. The obtained results are satisfactory when compared to the state-of-the-art DoS detection methods.

Keywords: DoS detection; Artificial Neural Networks; Feed-forward Neural Networks; Network traffic classification; Feature selection

Mohamed Idhammad, Karim Afdel and Mustapha Belouch, “DoS Detection Method based on Artificial Neural Networks” International Journal of Advanced Computer Science and Applications(IJACSA), 8(4), 2017. http://dx.doi.org/10.14569/IJACSA.2017.080461

@article{Idhammad2017,
title = {DoS Detection Method based on Artificial Neural Networks},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.080461},
url = {http://dx.doi.org/10.14569/IJACSA.2017.080461},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {4},
author = {Mohamed Idhammad and Karim Afdel and Mustapha Belouch}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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