Jaw Ebrima, Wang Xueming
College of Computer Science and Technology, Guizhou University, Guiyang, Guizhou, China.
School of Information Technology and Communication, University of The Gambia (UTG), Banjul, Peace Building, Kanifing, The Gambia.
PeerJ Comput Sci. 2022 Mar 2;8:e900. doi: 10.7717/peerj-cs.900. eCollection 2022.
The rapid advanced technological development alongside the Internet with its cutting-edge applications has positively impacted human society in many aspects. Nevertheless, it equally comes with the escalating privacy and critical cybersecurity concerns that can lead to catastrophic consequences, such as overwhelming the current network security frameworks. Consequently, both the industry and academia have been tirelessly harnessing various approaches to design, implement and deploy intrusion detection systems (IDSs) with event correlation frameworks to help mitigate some of these contemporary challenges. There are two common types of IDS: signature and anomaly-based IDS. Signature-based IDS, specifically, Snort works on the concepts of rules. However, the conventional way of creating Snort rules can be very costly and error-prone. Also, the massively generated alerts from heterogeneous anomaly-based IDSs is a significant research challenge yet to be addressed. Therefore, this paper proposed a novel Snort Automatic Rule Generator (SARG) that exploits the network packet contents to automatically generate efficient and reliable Snort rules with less human intervention. Furthermore, we evaluated the effectiveness and reliability of the generated Snort rules, which produced promising results. In addition, this paper proposed a novel Security Event Correlator (SEC) that effectively accepts raw events (alerts) without prior knowledge and produces a much more manageable set of alerts for easy analysis and interpretation. As a result, alleviating the massive false alarm rate (FAR) challenges of existing IDSs. Lastly, we have performed a series of experiments to test the proposed systems. It is evident from the experimental results that SARG-SEC has demonstrated impressive performance and could significantly mitigate the existing challenges of dealing with the vast generated alerts and the labor-intensive creation of Snort rules.
随着互联网及其前沿应用的快速技术发展,在许多方面对人类社会产生了积极影响。然而,它同样带来了不断升级的隐私和关键的网络安全问题,可能导致灾难性后果,比如使当前的网络安全框架不堪重负。因此,行业和学术界一直在不懈地采用各种方法来设计、实施和部署带有事件关联框架的入侵检测系统(IDS),以帮助缓解其中一些当代挑战。IDS有两种常见类型:基于特征的IDS和基于异常的IDS。具体来说,基于特征的IDS(如Snort)是基于规则的概念运行的。然而,创建Snort规则的传统方法可能成本很高且容易出错。此外,来自异构的基于异常的IDS大量生成的警报是一个尚未解决的重大研究挑战。因此,本文提出了一种新颖的Snort自动规则生成器(SARG),它利用网络数据包内容自动生成高效可靠的Snort规则,减少人工干预。此外,我们评估了生成的Snort规则的有效性和可靠性,取得了有前景的结果。另外,本文提出了一种新颖的安全事件关联器(SEC),它能有效接受无先验知识的原始事件(警报),并生成一组更易于管理的警报以便于分析和解释。结果,减轻了现有IDS的大量误报率(FAR)挑战。最后,我们进行了一系列实验来测试所提出的系统。从实验结果可以明显看出,SARG-SEC展示了令人印象深刻的性能,并且可以显著缓解处理大量生成的警报和人工密集型创建Snort规则的现有挑战。