Detecting DDoS Attack in Spam Emails using Density-Weight Model
Jianrong Zhang, Mao Lin Huang, DB Hoang · UTS ePRESS (University of Technology Sydney) · 2011
DDoS attacks whose are embedded in spam emails are increasingly becoming numerous and sophisticated in nature. Hence this has given a growing need for spam email analysis to identify these attacks. The uses of these intrusion detection systems have given rise to two new challenges, 1) how to incrase the accuracy of detection, 2) how to present large spam email networks for better understanding. In this paper we introduce a new analytical model that uses two coefficient vectors: 'density' and 'weight' to measure the network density and system workload for the analysis of DDoS attack of spam emails. We then use a visual clustering method to classify and display the spam emails for better understanding of the spam email network. The experiment shows that the proposed new model can increase the accuracy of the detection of DDoS attacks.