By Topic

The Optimistic Schemes of Cluster Analysis and k-NN Classifier Method in Detecting and Counteracting Learned DDoS Attack

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Edwin R. Ramos ; Dept. of Syst. Manage. Eng., INJE Univ., Gimhae ; Sooyoung Chae ; Mansig Kim ; Myeonggil Choi

The creation of Internet has been materialized to help people become aware of different information and unleash them from the state of ignorance. However, its vast expansions turned out to be a threat at their individual premises wherein integrity, accessibility and confidentiality are oftentimes compromised. This paper concerns the optimistic schemes of detecting and counteracting learned DDoS attacks. We described approaches of cluster analysis and k-NN classifier method as effective tools to battle tremendous security threats i.e., malicious usage, attacks and sabotage. These schemes were tested using a set of benchmark data from KDD (Knowledge Discovery and Data Mining) designed by DARPA. Results are clear evidence that combinations of such schemes lead to have an efficient and accurate performance in detecting DDoS attacks.

Published in:

2008 New Technologies, Mobility and Security

Date of Conference:

5-7 Nov. 2008