Adaptive Reputation Weights Assignment Scheme for MANETs

To detect and isolate the misbehaving nodes in MANETs, reputation-based security schemes have been developed. These schemes either utilize only first-hand (direct) information or both first-hand and second-hand (direct) information, to compute the trustworthiness of nodes. Both of the aforementioned categories have their own strengths and weaknesses under different network conditions and scenarios. An adaptive scheme is desired that can optimally utilize first and second hand information by assigning appropriate weights according to network conditions. In this paper, we first analyze the impact of different network conditions on first-hand and second-hand information based schemes. Then, we will argue for adaptive reputation weights assignment scheme. The proposed scheme adapts the weights given to first-hand and second-hand reputation information based on the run-time network conditions, such as number of interactions, node mobility, closeness measurement value, and average neighborhood trustworthiness, between communicating nodes. Proposed scheme is compared with one of the static reputation weights assignment schemes. Simulation results show that proposed scheme achieves significant improvements in detection rate and packet delivery ratio with low false positives rate in comparison to the static weights assignment scheme under varying network parameters.