Co-FQL: Anomaly detection using cooperative fuzzy Q-learning in network

Abstract

Wireless networks are increasingly overwhelmed by Distributed Denial of Service DDoS attacks by generating flooding packets that exhaust critical computing and communication resources of a victim's mobile device within a very short period of time. This must be protected. Effective detection of DDoS attacks requires an adaptive learning classifier, with less computational complexity, and an accurate decision making to stunt such attacks. We propose a distributed intrusion detection system called Cooperative IDS to protect wireless nodes within the network and target nodes from DDoS attacks by using a Cooperative Fuzzy Q-learning Co-FQL optimization algorithmic technique to identify the attack patterns and take appropriate countermeasures. The Co-FQL algorithm was trained and tested to establish its performance by generating attacks from the NSL-KDD and “CAIDA DDoS Attack 2007” datasets during the simulation experiments. Experimental results show that the proposed Co-FQL IDS has a 90.58% higher accuracy of detection rate than Fuzzy Logic Controller or Q-learning algorithm or Fuzzy Q-learning alone.