Reputation-based secure spectrum situation fusion in distributed cognitive radio networks

In distributed cognitive radio （CR） network, the cooperative spectrum situation sensing based on consensus scheme may be disrupted by spectrum sensing data falsification （SSDF） attacks. In this paper, a secure spectrum situation fusion scheme based on reputation is proposed to counter attacks. The neighboring nodes of secondary users （SUs） get the corresponding dynamic trust value according to their behavior, which restrict the impact of the malicious behavior on the premise to ensure information interaction of normal nodes. Theoretical analysis and simulation results show that the consensus fusion scheme based on reputation has better performance than the existing algorithm which eliminates the neighboring node with the biggest deviation value from mean value. It demonstrates that the proposed scheme not only achieves better convergence properties but also has higher detection probability than the existing scheme in the process of spectrum situation fusion.

Weight sequential log-likelihood ratio detect algorithm with malicious users removing

Due to the openness of the cognitive radio network, spectrum sensing data falsification （SSDF） can attack the spectrum sensing easily, while there is no effective algorithm proposed in current research work, so this paper introduces the malicious users removing to the weight sequential probability radio test （WSPRT）. The terminals＇ weight is weighted by the accuracy of their spectrum sensing information, which can also be used to detect the malicious user. If one terminal owns a low weight, it can be treated as malicious user, and should be removed from the aggregation center. Simulation results show that the improved WSPRT can achieve higher performance compared with the other two conventional sequential detection methods under different number of malicious users.

On the Security of Information Dissemination in the Internet-of-Vehicles

Internet of Vehicles（IoV） is regarded as an emerging paradigm for connected vehicles to exchange their information with other vehicles using vehicle-to-vehicle（V2V） communications by forming a vehicular ad hoc networks（VANETs）, with roadside units using vehicle-to-roadside（V2R） communications. IoV offers several benefits such as road safety, traffic efficiency, and infotainment by forwarding up-to-date traffic information about upcoming traffic. For instance, IoV is regarded as a technology that could help reduce the number of deaths caused by road accidents, and reduce fuel costs and travel time on the road. Vehicles could rapidly learn about the road condition and promptly respond and notify drivers for making informed decisions. However, malicious users in IoV may mislead the whole communications and create chaos on the road. Data falsification attack is one of the main security issues in IoV where vehicles rely on information received from other peers/vehicles. In this paper,we present data falsification attack detection using hashes for enhancing network security and performance by adapting contention window size to forward accurate information to the neighboring vehicles in a timely manner（to improve throughput while reducing end-to-end delay）. We also present clustering approach to reduce travel time in case of traffic congestion. Performance of the proposed approach is evaluated using numerical results obtained from simulations. We found that the proposed adaptive approach prevents IoV from data falsification attacks and provides higher throughput with lower delay.