(35.170.81.33)
[ij] [ij] [ij] 
Email id
 

Asian Journal of Research in Social Sciences and Humanities
Year : 2017, Volume : 7, Issue : 3
First page : ( 1343) Last page : ( 1353)
Online ISSN : 2249-7315.
Article DOI : 10.5958/2249-7315.2017.00247.7

Multi Model Network Analysis for Improved Intrusion Tracing towards Mitigating DDoS Attack

 Baskar*, Gnanasekaran T.**

*K C G College of Technology, Karapakkam, Chennai, Tamilnadu, India. baashkarinfo@yahoo.co.in

**R.M.K. College of Engineering and Technology, Puduvoyal, Chennai, Tamilnadu, India. t.gnanasekaran@gmail.com

Online published on 23 March, 2017.

Abstract

The problem of distributed denial of service attacks has been considered for several environments and number of approaches has been described. The basic analysis of flow rates does not help in modern attacks. To improve the performance of mitigating denial of service attacks, a novel multi model frequency analysis scheme has been described in this paper. The traffic approximation is required in different time domain as the modern attacks have been carried out in various strategic conditions. Similarly the method performs stream analysis which considers the payload data of different requests produced by various clients. Further the method performs the frequency approximation which counts the number of request being produced towards various servers. Using all the approximated values of different factors, the method compute the multi mode trust measure and decide the trustworthy of the request. The method produces efficient results in intrusion detection and accuracy in mitigating denial of service attacks.

Top

Keywords

Traffic Approximation, Stream Analysis, Frequency Approximation, DDoS attack.

Top

  
║ Site map ║ Privacy Policy ║ Copyright ║ Terms & Conditions ║ Page Rank Tool
812,712,394 visitor(s) since 30th May, 2005.
All rights reserved. Site designed and maintained by DIVA ENTERPRISES PVT. LTD..
Note: Please use Internet Explorer (6.0 or above). Some functionalities may not work in other browsers.