DDoS Detection Using Modified K-Means Clustering with Chain Initialization Over Landmark Window

Denial-of-service is a common form of network attack that affect user access right by preventing legitimate user from accessing certain information, thus giving great disadvantage to the user and service provider. This paper present a method of denial-of-service detection using clustering technique with k-means algorithm which available to be modified and developed in many possible way. K-means algorithm used in this paper is modified using chain initialization over landmark window approach to process large amount of data and the result evaluated with detection rate, accuracy, and false positive rate. This method has been proven effective in detecting denial-of-service traffic using DARPA 98 dataset with satisfying result.