Detection Method Of Flooding Attack In VANET Based On Traffic Self-similar Model

With the development of wireless communication technology and the intelligent evolution of vehicles,VANET(Vehicle Ad hoc Network)has quickly become a research hotspot in the field of network technology.As an important part of the future intelligent transportation system,VANET not only provides people with comfortable driving experience and safe driving guarantee,but also improves urban traffic dispatching capacity and traffic efficiency.However,in VANET,vehicles are driving fast,and the network topology is unstable,so VANET is extremely vulnerable to security attacks.Flooding attacks' consequences are very serious,which may lead to serious traffic accidents.In view of the current security problems faced by VANET,it is necessary to effectively detect flooding attacks to mitigate the adverse effects of flooding attacks in VANET.Aiming at the security problem of the above-mentioned flooding attacks,and considering the complex network environment and high requirements for detection efficiency in VANET,this paper proposes a lightweight traffic anomaly detection method based on Hurst self-similar parameters.This article mainly does the following:(1)Analyze the concepts and principles of flooding attacks and traffic self-similarity detection research,summarize the anomaly detection mechanism for VANET traffic model;(2)Detailed analysis of the detection principle and process based on self-similar parameters of traffic in this paper,and accurate construction of VANET traffic model;(3)A new Hurst calculation method based on sliding windows is proposed.This method divides a long data sequence into shorter sequences according to the sliding window,and then uses the R/S method to calculate the H value.The sequence of H values calculated by sliding windows in turn can obtain the self-similarity curve;(4)Detect the flow self-similarity curve on the roadside unit(RSU)and find that the flow changes abnormally;(5)Count the data packets during abnormal periods to detect malicious nodes that launch flooding attacks.The simulation results of the actual network traffic of VANET show that the improved H-value algorithm can adapt to long data sequences and can guarantee the calculation speed and accuracy.The traffic anomaly detection method proposed in this paper not only can effectively detect abnormal traffic under a single attack of different strengths,but also has a good ability to detect abnormal traffic under multiple attacks.This method can not only alleviate the computing processing pressure of RSU and Cloud,but also reduce the network bandwidth pressure brought by the detection mechanism.