A Bayesian Statistical Model to Alleviate Greediness in Wireless Mesh Networks

Wireless mesh Networks (WMNs) are a prominent paradigm of wireless communication that have been widely used in many applications. The growing popularity of such networks opened the door to a profusion of attacks that may target their core functioning leading to a harmful impact on their performance. Hence, the need of robust and fast detection of those attacks became a major prerequisite in order to guarantee an efficient and fair share of network resources among nodes. One of the well known devastating attacks is MAC layer misbehavior which may lead to severe collapse of network performance. In this study, we focus on such misbehavior and in particular on the adaptive greedy behavior of a node in wireless mesh network environment. In such environment, wireless nodes compete to gain access to the medium in order to communicate with a mesh router (MR). In this case, a greedy node may violate the MAC protocol rules to earn extra bandwidth share upon its neighbors. To evade from detection, the cheater node may use more than one technique and switch dynamically between each of them. To counter such misuse, we propose to extend our previous solution, dubbed FLSAC, through the use of a Bayesian statistical model. This new scheme is implemented in conjunction with FLSAC at the mesh router/gateway to monitor the behavior of the attached wireless mesh clients and detect any deviation from the proper protocol rules. The simulation results reveal that this new solution outperforms both of DOMINO and FLSAC in terms of detection rate and accuracy.