A comparative experimental design and performance analysis of Snort-based Intrusion Detection System in practical computer networks

As one of the most reliable technologies, network intrusion detection system (NIDS) allows the monitoring of incoming and outgoing traffic to identify unauthorised usage and mishandling of attackers in computer network systems. To this extent, this paper investigates the experimental performance of Snort-based NIDS (S-NIDS) in a practical network with the latest technology in various network scenarios including high data speed and/or heavy traffic and/or large packet size. An effective testbed is designed based on Snort using different muti-core processors, e.g., i5 and i7, with different operating systems, e.g., Windows 7, Windows Server and Linux. Furthermore, considering an enterprise network consisting of multiple virtual local area networks (VLANs), a centralised parallel S-NIDS (CPS-NIDS) is proposed with the support of a centralised database server to deal with high data speed and heavy traffic. Experimental evaluation is carried out for each network configuration to evaluate the performance of the S-NIDS in different network scenarios as well as validating the effectiveness of the proposed CPS-NIDS. In particular, by analysing packet analysis efficiency, an improved performance of up to 10% is shown to be achieved with Linux over other operating systems, while up to 8% of improved performance can be achieved with i7 over i5 processors

A comparative experimental design and performance analysis of Snort-based Intrusion Detection System in practical computer networks

As one of the most reliable technologies, network intrusion detection system (NIDS) allows the monitoring of incoming and outgoing traffic to identify unauthorised usage and mishandling of attackers in computer network systems. To this extent, this paper investigates the experimental performance of Snort-based NIDS (S-NIDS) in a practical network with the latest technology in various network scenarios including high data speed and/or heavy traffic and/or large packet size. An effective testbed is designed based on Snort using different muti-core processors, e.g., i5 and i7, with different operating systems, e.g., Windows 7, Windows Server and Linux. Furthermore, considering an enterprise network consisting of multiple virtual local area networks (VLANs), a centralised parallel S-NIDS (CPS-NIDS) is proposed with the support of a centralised database server to deal with high data speed and heavy traffic. Experimental evaluation is carried out for each network configuration to evaluate the performance of the S-NIDS in different network scenarios as well as validating the effectiveness of the proposed CPS-NIDS. In particular, by analysing packet analysis efficiency, an improved performance of up to 10% is shown to be achieved with Linux over other operating systems, while up to 8% of improved performance can be achieved with i7 over i5 processors

On Application Layer DDoS Attack Detection in High-Speed Encrypted Networks

Application-layer denial-of-service attacks have become a serious threat to modern high-speed computer networks and systems. Unlike network-layer attacks, application-layer attacks can be performed by using legitimate requests from legitimately connected network machines which makes these attacks undetectable for signature-based intrusion detection systems. Moreover, the attacks may utilize protocols that encrypt the data of network connections in the application layer making it even harder to detect attacker’s activity without decrypting users network traffic and violating their privacy. In this paper, we present a method which allows us to timely detect various applicationlayer attacks against a computer network. We focus on detection of the attacks that utilize encrypted protocols by applying an anomaly-detection-based approach to statistics extracted from network packets. Since network traffic decryption can violate ethical norms and regulations on privacy, the detection method proposed analyzes network traffic without decryption. The method involves construction of a model of normal user behavior by analyzing conversations between a server and clients. The algorithm is self-adaptive and allows one to update the model every time when a new portion of network traffic data is available. Once the model has been built, it can be applied to detect various types of application-layer denial-of- service attacks. The proposed technique is evaluated with realistic end user network traffic generated in our virtual network environment. Evaluation results show that these attacks can be properly detected, while the number of false alarms remains very low

Performance comparison of intrusion detection systems and application of machine learning to Snort system

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