Anomaly-based Intrusion Detection System Using Fuzzy Logic
This work addresses the need for effective detection methods against DDOS attacks, which disrupt services for end-users, but it appears incremental as it combines existing techniques like fuzzy logic and feature selection.
The paper tackled the problem of detecting Distributed Denial of Service (DDOS) attacks by introducing an anomaly-based intrusion detection system using fuzzy logic, achieving a true-positive rate of 91.1% and a false-positive rate of 0.006% on an open-source dataset.
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.