DoS Detection Method based on Artificial Neural Networks
| Autor: | Karim Afdel, Mustapha Belouch, Mohamed Idhammad |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
General Computer Science
Artificial neural network Computer science business.industry Feature selection 02 engineering and technology 010402 general chemistry Machine learning computer.software_genre 01 natural sciences 0104 chemical sciences 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining Artificial intelligence business computer Selection (genetic algorithm) |
| Zdroj: | International Journal of Advanced Computer Science and Applications. 8 |
| ISSN: | 2156-5570 2158-107X |
| DOI: | 10.14569/ijacsa.2017.080461 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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