Anomaly Detection Using Cooperative Fuzzy Logic Controller

Abstract

This paper presents an Intrusion Detection System (IDS) with the integration of multi agent systems and artificial intelligence techniques such as fuzzy logic controller (FLC), multi-layer perceptron (MLP) and adaptive neuro-fuzzy inference system (ANFIS). The paper introduces Network Intrusion Detection Systems (NIDS), which monitors the network traffic and detect any possible attacks. The system is made up of three agents: accumulator, analyser and decision maker agents. The accumulator agent works to gather and filter network traffics. The analyser agent uses decision tree (DT) to classify the data. Finally, the decision maker agent uses fuzzy logic controller (FLC) to make the final decision. The proposed system was simulated using KDDCup 1999 dataset and the experimental results show an improvement of the attack detection accuracy to 99.95% and false alarm rate of 1%. © Springer-Verlag Berlin Heidelberg 2013.