Paper Title
Snort Log Analysis With Data Mining And Visualization

with the growing sophistication of cyber attacks, it has become necessary to combine techniques such as data mining into cyber security. However, the utilization of techniques such as association rule mining is still an open challenge in the context of cyber security. This study proposes the use of association rule mining to be applied to Snort logs before signature matching as primary check in order to detect intrusions. With association rules, it is possible to gain valuable insight within Snort logs in order to find key relationships. On the other hand, given that a large number of logs can be generated in Snort, this creates a possibility for identifying a large number of association rules which can make the process of analysis challenging for a user. Therefore, this study extends itself to integrate the process of association rule mining with data visualization to create a better representation of patterns discovered. Index Terms— Association Rule Mining, Data Visualization, Intrusion Detection.