The most important aspect in IoT is security. The provision of security in IoT systems is the responsibility of a trust management mechanism. However a trust management mechanism comprises a number of components, of which secure routing is vital among them. There are a number of effective parameters in secure routing which have been considered in the presented multi-objective optimization model. In this paper, MultiObjective Learning Automata (MOLA) was used to solve secure routing problem which can simultaneously optimize all parameters. There exist three methods of training LA, and the results of the different methods were compared in this study. The proposed approach can be used with both administrator and users because their requirements are considered in a model and it is quite easy for administrator and users to comprehend.

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In this article, we propose a novel method that uses vulnerability evidence reasoning in network forensics analysis. Central to our method is the evidence graph model to support evidence presentation and reasoning. Based on the evidence graph, we propose a network forensics method that built the evidence graph on the basis of the network system vulnerabilities and environmental information. At the same time, the proposed method can realize the reconstruction of attack scenarios with high efficiency and with the capability of identifying multi-staged attacks through evidence reasoning. Results of the experiment that we conducted would show that the proposed method is complete and credible with certain reasoning ability, which can be a powerful tool for rapid and effective network forensic analysis.