Research And Implementation Of Intrusion Detection Technology For Internet Of Vehicle Based On Machine Learning

With the development of computing technology and Internet technology,the traditional Vehicle Ad-hoc Networks is now transforming into the Internet of Vehicles.At the same time,machine learning and deep learning have made breakthroughs in many fields,such as computer vision,natural language processing,speech recognition,recommendation systems,machine translation,as well as dialogue systems.As more and more artificial intelligence technologies are applied to smart car networking,the security and reliability of the car networking system pose a huge challenge.Vehicle networking has its own particularity: the hardware resources of terminal in vehicles are tightly structured,the computing capacity is relatively weak,furthermore,the environment is complex,and the security requirements are extremely high.How to use artificial intelligence techniques such as machine learning to design an Intrusion Detection System suitable for the special environment of the Internet of Vehicles is the focus of research in recent years.Therefore,for these issues,the main research work of this paper includes:(1)In view of the characteristics of computing power and storage capacity of the Internet of Vehicles,we research on data dimensionality reduction technology,and propose data reduction using AutoEncoder network;(2)We study machine learning and deep learning models,and propose a deep fusion model scheme,which improves the classification ability of the Internet of Vehicles Intrusion Detection System without introducing too much additional computation and storage overhead;(3)Design and implement the TEE outsourcing computing and security enhancement architecture to ensure the security and reliability of the vehicle network intrusion detection model running environment,and combine TEE and REE through outsourcing computing to solve TEE's reference performance issues due to the high security of it;(4)Design and implement a highly scalable vehicle network intrusion detection system.The outsourcing computing framework naturally supports a variety of vehicle terminals,which is suitable for different hardware platforms and users' business needs with good scalability adaptability and reliability.The chapters of this paper are arranged as follows: The first chapter introduces the background and significance of this paper and gives the key points and innovations of this paper in details.The second chapter introduces the research and engineering implementation approach including the technical and theoretical basis of the approach.The third chapter focuses on the research of the Internet of Vehicles intrusion detection model,including key innovations such as data dimension reduction,model fusion,security enhancement,and outsourcing computing.The fourth chapter is the engineering part of this paper.It gives details of the implementation of the IDS system and mainly focuses on the main modules.The fifth chapter is about the test and results in the analysis of Internet of Vehicles IDS,including the performance of the intrusion detection model under actual attacks,as well as security enhancements and performance improvements from outsourcing computing architectures.