DoS Detection Method based on Artificial Neural Networks

Apply

DoS Detection Method based on Artificial Neural Networks

Journal Title: International Journal of Advanced Computer Science & Applications - Year 2017, Vol 8, Issue 4

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.

Authors and Affiliations

Mohamed Idhammad, Karim Afdel, Mustapha Belouch

Keywords

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

EP ID EP258388
DOI 10.14569/IJACSA.2017.080461
Views 109
Downloads 0