Performance Comparison of Intrusion Detection Systems and Application of Machine Learning to Snort System (1710.04843v2)

Abstract: 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.

• The paper evaluates the performance of Snort and Suricata intrusion detection systems, proposing machine learning applications to enhance Snort's accuracy and mitigate high false positive rates.
• The study found Suricata handles higher network speeds with fewer packet drops but uses significantly more resources than Snort.
• Machine learning techniques, especially SVM, were applied to Snort, with hybrid and optimized SVM methods effectively improving detection accuracy and achieving a notably low false positive rate of 8.6%.

Analysis of IDS Performance and Machine Learning Application to Snort

In the paper "Performance Comparison of Intrusion Detection Systems and Application of Machine Learning to Snort System," the authors Syed Ali Raza Shah and Biju Issac evaluate the efficacy of two prominent open-source intrusion detection systems (IDSs)—Snort and Suricata—and propose enhancements using machine learning algorithms. Given the increasing reliance on computer networks for critical functions, precise differentiation between legitimate and malicious traffic is imperative. This paper sheds light on the operational disparities between Snort's single-thread architecture and Suricata's multi-thread architecture, while also advocating for machine learning integration to mitigate Snort’s high false positive rate.

Core Findings

The comparative evaluation reveals that while Suricata can handle higher network speeds with lower packet drop rates than Snort, it demands significantly more computational resources. Snort, selected for further experimentation due to its higher detection accuracy, suffers from a pronounced false positive alarm rate. Leveraging machine learning to address this, the paper evaluates several algorithms, with Support Vector Machine (SVM) emerging as the preeminent choice. Notably, a hybrid SVM and Fuzzy logic approach, and an optimized SVM utilizing the firefly algorithm, demonstrated improved detection accuracy and reduced false alarm rates, achieving a notably low false positive rate of 8.6%.

Implications

The implications of this research are substantial both in technical and practical dimensions. For practitioners deploying IDSs, this insight offers a roadmap to balancing performance and resource utilization. The integration of machine learning techniques with traditional IDS architectures can enhance threat detection capabilities, thereby fortifying network security infrastructures against evolving threats. Additionally, the findings underscore the importance of hybrid and optimized machine learning methods to refine IDS operation further.

Future Perspectives

Looking ahead, these findings invite further exploration into other IDS platforms while considering additional machine learning models that could enhance detection capabilities. The impending multithreaded Snort release could dramatically change the landscape, potentially making Snort more competitive in environments requiring high-speed packet processing. Another promising avenue is fine-tuning parameters of hybrid models for specific network conditions.

In summary, the paper establishes a foundational approach to using hybrid machine learning algorithms in open-source IDS systems, emphasizing performance efficiency and detection accuracy. Through empirical analysis, the authors advocate for continuous improvement and adaptability, key traits required to combat sophisticated cyber threats effectively.