FADE: Forwarding Assessment Based Detection of Collaborative Grey Hole Attacks in WMNs

Data security, which is concerned with the confidentiality, integrity and availability of data, is still challenging the application of wireless mesh networks (WMNs). In this paper, we focus on a special type of denial-of-service attack, called selective forwarding or grey hole attack. When this attack is launched at the gateways of a WMN where data tend to aggregate, it could lead to severe damages due to loss of sensitive data. Most existing proposals that focus on detecting stand-alone attackers via channel overhearing are ineffective against collusive attackers. In this paper, we propose a forwarding assessment based detection (FADE) scheme to mitigate collaborative grey hole attacks. Specifically, FADE detects sophisticated attacks by means of forwarding assessments aided by two-hop acknowledgement monitoring. Moreover, FADE can coexist with contemporary link security techniques. We analyze the optimal detection threshold that minimizes the sum of false positive rate and false negative rate of FADE, considering the network dynamics due to degraded channel quality or medium access collisions. Extensive simulation results are presented to demonstrate the adaptability of FADE to network dynamics and its effectiveness in detecting collaborative grey hole attacks.