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