Anomaly-based Intrusion Detection System Using Fuzzy Logic

Mohammad Almseidin, Jamil Al‐Sawwa, Mouhammd Alkasassbeh

202131 citationsDOI

Abstract

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.

Topics & Concepts

Denial-of-service attackIntrusion detection systemAnomaly detectionComputer scienceFuzzy logicData miningAnomaly-based intrusion detection systemAdaptive neuro fuzzy inference systemAnomaly (physics)InferenceArtificial intelligenceFuzzy inference systemFuzzy control systemMachine learningThe InternetCondensed matter physicsWorld Wide WebPhysicsNetwork Security and Intrusion DetectionAdvanced Malware Detection TechniquesAnomaly Detection Techniques and Applications