Recently, the Distributed Denial of Service (DDOS) attacks has been used for different aspects to denial the number of services for the end-users. Therefore, there is an urgent need to design an effective detection method against this type of attack. A fuzzy inference system offers the results in a more readable and understandable form. This paper introduces an anomaly-based Intrusion Detection (IDS) system using fuzzy logic. The fuzzy logic inference system implemented as a detection method for Distributed Denial of Service (DDOS) attacks. The suggested method was applied to an open-source DDOS dataset. Experimental results show that the anomaly-based Intrusion Detection system using fuzzy logic obtained the best result by utilizing the InfoGain features selection method besides the fuzzy inference system, the results were 91.1% for the true-positive rate and 0.006% for the false-positive rate.

中文翻译：

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使用模糊逻辑的基于异常的入侵检测系统

最近，分布式拒绝服务 (DDOS) 攻击已被用于不同方面以拒绝最终用户的服务数量。因此，迫切需要设计一种针对此类攻击的有效检测方法。模糊推理系统以更具可读性和可理解性的形式提供结果。本文介绍了一种使用模糊逻辑的基于异常的入侵检测 (IDS) 系统。模糊逻辑推理系统作为分布式拒绝服务 (DDOS) 攻击的检测方法实施。建议的方法应用于开源 DDOS 数据集。实验结果表明，使用模糊逻辑的异常入侵检测系统除了模糊推理系统外，还利用InfoGain特征选择方法获得了最好的结果，结果为91。