CRM: a new dynamic cross-layer reputation computation model in wireless networks

Abstract

Multi-hop wireless networks (MWNs) have been widely accepted as an indispensable component of next-generation communication systems due to their broad applications and easy deployment without relying on any infrastructure. Although showing huge benefits, MWNs face many security problems, especially the internal multi-layer security threats being one of the most challenging issues. Since most security mechanisms require the cooperation of nodes, characterizing and learning actions of neighboring nodes and the evolution of these actions over time is vital to construct an efficient and robust solution for security-sensitive applications such as social networking, mobile banking, and teleconferencing. In this paper, we propose a new dynamic cross-layer reputation computation model named CRM to dynamically characterize and quantify actions of nodes. CRM couples uncertainty based conventional layered reputation computation model with cross-layer design and multi-level security technology to identify malicious nodes and preserve security against internal multi-layer threats. Simulation results and performance analyses demonstrate that CRM can provide rapid and accurate malicious node identification and management, and implement the security preservation against the internal multi-layer and bad mouthing attacks more effectively and efficiently than existing models.