Research On Clustering And Abnormal Traffic Detection Algorithm Of Mobile Ad Hoc Network For Security Protection

Traditional wired and wireless networks are limited by cables and base stations,and are not suitable for flexible and dynamic networking scenarios.The mobile ad hoc network can form a mobile temporary multihop,dynamic topology,self-organizing and centerless mobile communication network through a plurality of wireless communication devices,which can well meet the above requirements.However,because of its own characteristics,the mobile ad hoc network is more vulnerable to network attacks,so it is particularly important to ensure the security of the mobile ad hoc network.This paper investigates and analyzes the security threats and security requirements of mobile ad hoc networks,and finds that the current network security risks are mainly attacks on routing protocols and intrusions of external abnormal traffic.On this basis,this paper designs two lines of defense of the mobile ad hoc network security protection mechanism.The first line of defense ensures the security and stability of routing and clustering protocols.First,a link reliability routing algorithm and a clustering algorithm based on security weighting are designed and implemented.The application of Markov chains and reinforcement learning to routing protocols can improve the reliability of data packet transmission in the network;in the weighted clustering algorithm The security reputation mechanism is introduced,taking into account the comprehensive evaluation results of the node itself and other neighbor nodes,and cooperating with the network access control and identity authentication provided by the trusted authentication access center,which effectively improves the cooperation of nodes and the security of clustered networks.The second line of defense is designed to efficiently and accurately detect abnormal behavior of network traffic.Firstly,an abnormal intrusion detection model is constructed based on the security cluster structure.The nodes in the cluster are divided into roles according to their functions and security,and the traffic collection and analysis and alarm work are carried out collaboratively.The detection method combining online and offline is adopted to improve the detection efficiency.In the detection algorithm,the random forest algorithm based on multiclassifier in machine learning is adopted,which reduces the computational cost and damage risk of single-point detection,and can flexibly configure the number of base classifiers to improve the detection accuracy.Through simulation analysis,it is verified that the algorithm is not only functionally superior to the traditional routing algorithm,but also shows good security performance in the face of network attack risks.Finally,this paper designs and implements the mobile ad hoc network security simulation system.Firstly,the system is analyzed and designed in detail.The system is divided into routing clustering function module,abnormal traffic detection module and visualization module.The algorithms proposed in this paper are simulated and verified one by one,and the functions of the system are verified through system testing verify.