DDTMS: Dirichlet-Distribution-Based Trust Management Scheme in Internet of Things

Abstract

Information security is important for the Internet of Things (IoT), the security of front-end information is especially critical. With this consideration, the integrity and authenticity of sensed information directly impacts the results of back-end big data and cloud computing. The front end of the IoT faces many security threats. In these security threats, internal attacks cannot be defended by traditional security schemes, such as encryption/decryption, authentication, and so on. Our contribution in this paper is that a DirichletDistribution-based Trust Management Scheme (DDTMS) in IoT is proposed to defend against the internal attacks. The novelty of our scheme can be summed up in two aspects. The first aspect considers the actual physical channel to extend the node behaviors from success and failure to success, failure, and uncertainty, meanwhile, the corresponding behaviors are weighted by using <ws, wf, wu>, in order to limit the measurement of each behavior by custom. In the second aspect, we introduce a third-party recommendation to calculate the trust value more acurrately. The simulated results demonstrate that DDTMS is better than the other two reputation models (Beta distribution and Gaussian distribution),and can more accurately describe the reputation changes to detect the malicious node quickly.