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