Biblio

With the rapid proliferation of Internet of Thing (IoT) devices, many security challenges could be introduced at low-end routers. Misbehaving routers affect the availability of the networks by dropping packets selectively and rejecting data forwarding services. Although existing Reputation Management (RM) systems are useful in identifying misbehaving routers, the centralized nature of the RM center has the risk of one-point failure. The emerging blockchain techniques, with the inherent decentralized consensus mechanism, provide a promising method to reduce this one-point failure risk. By adopting the distributed consensus mechanism, we propose a blockchain-based reputation management system in IoT networks to overcome the limitation of centralized router RM systems. The proposed solution utilizes the blockchain technique as a decentralized database to store router reports for calculating reputation of each router. With the proposed reputation calculation mechanism, the reliability of each router would be evaluated, and the malicious misbehaving routers with low reputations will be blacklisted and get isolated. More importantly, we develop an optimized group mining process for blockchain technique in order to improve the efficiency of block generation and reduce the resource consumption. The simulation results validate the distributed blockchain-based RM system in terms of attacks detection and system convergence performance, and the comparison result of the proposed group mining process with existing blockchain models illustrates the applicability and feasibility of the proposed works.

Vehicular ad hoc networks (VANETs) have become a promising technology in intelligent transportation systems (ITS) with rising interest of expedient, safe, and high-efficient transportation. VANETs are vulnerable to malicious nodes and result in performance degradation because of dynamicity and infrastructure-less. In this paper, we propose a trust based dueling deep reinforcement learning approach (T-DDRL) for communication of connected vehicles, we deploy a dueling network architecture into a logically centralized controller of software-defined networking (SDN). Specifically, the SDN controller is used as an agent to learn the most trusted routing path by deep neural network (DNN) in VANETs, where the trust model is designed to evaluate neighbors' behaviour of forwarding routing information. Simulation results are presented to show the effectiveness of the proposed T-DDRL framework.