DoS Detection Method based on Artificial Neural Networks

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.

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