Abstract: A Denial - of - Service attack (DoS) is when someone tries to stop someone else from viewing parts of the internet. To avoid this problem earlier uses Multivariate Correlation Analysis (MCA) for accurate network traffic characterization by extracting the geometrical correlations between network traffic features. Our MCA - based DoS attack detection system employs the principle of anomaly - based detection in attack recognition.. Neuro - fuzzy sytems is proposed as subsystems of the ensemble. Sugeno type Neuro - Fuzzy Inference System has been chosen as a base classifier for our research. Single classifier makes error on different training samples. So, by creating the classifiers and combining their outputs, the total amount of error can be reduced and the detection accuracy can be increased. The proposed Adaptive Neuro -Fuzzy Inference based system will be able to detect an intrusion behavior of the networks. The experiments and the evaluations of the proposed method were performed. The results show that our system outperforms two other previously developed states- of- the-art approaches in terms of detection accuracy.
Keywords: Denial of service, Multivariate correlation analysis.
Title: An Efficient Way of Denial of Service Attack Detection Based on Triangle Map Generation
Author: Shanofer. S
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
Research Publish Journals
