Real-Time MAC-Layer Selfish Misbehavior Detection and Prevention Technique for Wireless Networks

Background/Objectives: Contention issue in the IEEE 802.11 based wireless network is resolved using CSMA/CA protocol that aims to ensure the Fair Share of the channel to each node in the network. A malicious node can manipulate the backoff parameters and gain large share of network access. Methods/Statistical Analysis: Since back off parameters are programmable field it can change the value any time. So real time detection of malicious behavior is required. For Real Time Detection a Markov chain-based analytical model is being used. Since the Despite of all its advantages there have been no mechanism used to monitor the MAC layer misbehavior of all the nodes in the wireless network. Only tagged nodes are being monitored. Findings: Adding to the challenge of identifying and isolating the malicious node from the network is the non-deterministic nature and distributed functionality of IEEE 802.11 based networks. In order to overcome the problem regarding the defensive mechanism and for better efficiency, this Research work has developed an efficient technique called Markov-RED-FT. By using this model all the nodes in the network can be monitored for malicious misbehavior and the flow trust value is calculated to penalize the malicious node. This proposed Markov-RED-FT employs the flow trust value to safe guard the legitimate flows. Malicious flows would be with lower trust values while legitimate flows would be with higher ones. Application/Improvements: This research work implemented the proposed Markov-RED-FT and from the results, it is established that the proposed model is performing well as compared with the existing model in terms of average detection delayand network throughput.