Research On The Key Technologies Of Intrusion Detection In Vehicular Network

Nowadays,autonomous vehicle has gradually entered people's vision,filling in the essential gap of transportation for future intelligent life.Vehicular Network is used as the underlying communication system in intelligent transportation to provide managers,drivers,and passengers with all necessary network supports and services.In these services,vehicle information related to the personal safety of passengers and drivers,sensitive information concerning personal privacy of passengers and information is provided to city managers about the correct road conditions.How to protect such information from malicious use,thus it can be seen,the research on the security is the most important part in the research of vehicle network.The current mainstream providing information verification and network security management is intrusion detection systems.However,this system has not only a large energy consumption but also delay and load impact on network performance.In addition,due to the highly dynamic and distributed nature of the vehicular network,intrusion detection systems cannot work as efficiently as the systems in the backbone network.Therefore,how to make the intrusion detection system in the vehicular network work with lower energy consumption and higher accuracy becomes a significant issue.On the issue,aiming to improve detection accuracy and reduce system consumption,the specific research content and innovation content of this article are as follows.As vehicular network is high dynamic and distributed,a stable clustering algorithm is proposed to make the intrusion detection system operate more efficiently.The proposed clustering scheme increased the life time of clustering and decreased the number of times that nodes switch from one to another cluster.At the same time,the problem that the cluster head may be a malicious node in the clustering algorithm is also considered.Not only the parameters related to stability,but also the index of the node honor value is included in the election of the cluster head.The detection performance of the system is optimized by combining the clustering algorithm with some special parameters in the intrusion detection system.The experimental results show that the new algorithm has been greatly improved in stability,which provides a necessary basis for the efficient operation of intrusion detection systems.In addition,the situation in which the cluster head node is a malicious node is also avoided.Based on the stable clustering algorithm above,the existing hybrid game-based intrusion detection method is further improved.By changing the calculation rules of its honor value,the problem that malicious nodes can cheat the cluster head detection by earning honor values through short-term good actions is also solved.Because stable clustering can make the nodes stay in the same cluster for a longer time,the cluster head also has more historical data to measure the quality of the nodes,which improved the detection accuracy in light mode and in hybrid mode.It also reduces the overall energy cost of the intrusion detection system.The experimental results show that the performance of the original algorithm has been improved a lot.