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This study investigates the performance of two open source intrusiondetection systems (IDSs) namely Snort and Suricata for accurately detecting themalicious traffic on computer networks. Snort and Suricata were installed ontwo different but identical computers and the performance was evaluated at 10Gbps network speed. It was noted that Suricata could process a higher speed ofnetwork traffic than Snort with lower packet drop rate but it consumed highercomputational resources. Snort had higher detection accuracy and was thusselected for further experiments. It was observed that the Snort triggered ahigh rate of false positive alarms. To solve this problem a Snort adaptiveplug-in was developed. To select the best performing algorithm for Snortadaptive plug-in, an empirical study was carried out with different learningalgorithms and Support Vector Machine (SVM) was selected. A hybrid version ofSVM and Fuzzy logic produced a better detection accuracy. But the best resultwas achieved using an optimised SVM with firefly algorithm with FPR (falsepositive rate) as 8.6% and FNR (false negative rate) as 2.2%, which is a goodresult. The novelty of this work is the performance comparison of two IDSs at10 Gbps and the application of hybrid and optimised machine learning algorithmsto Snort.

Performance Comparison of Intrusion Detection Systems and Application of Machine Learning to Snort System