Visual analytics for intrusion detection in spam emails

Spam email attacks are increasing at an alarming rate and have become more and more cunning in nature. This has necessitated the need for visual spam email analysis within an intrusion detection system to identify these attacks. The challenges are how to increase the accuracy of detection and how to visualise large volumes of spam email to better understand the analysis results and identify email attacks. This paper proposes a Density-Weight model that is to strengthen and extend the system capacity for analysis of network attacks in spam emails, including DDoS attacks. An interactive visual clustering method DA-TU is introduced to classify and display spam emails. The experimental results have shown that the proposed new model has improved the accuracy of intrusion detection and provides a better understanding of the nature of spam email attacks on though the network.

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