| Title | An Internet of Vehicles Intrusion Detection System Based on a Convolutional Neural Network |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Peng, Ruxiang, Li, Weishi, Yang, Tao, Huafeng, Kong |
| Conference Name | 2019 IEEE Intl Conf on Parallel Distributed Processing with Applications, Big Data Cloud Computing, Sustainable Computing Communications, Social Computing Networking (ISPA/BDCloud/SocialCom/SustainCom) |
| Date Published | dec |
| Keywords | automobiles, car network, computer network security, convolution, convolutional neural nets, convolutional neural network, convolutional neural network (CNN), convolutional neural networks, cryptography, cyber physical systems, feature extraction, Human Behavior, human factors, IDS, Information security, Internet, Internet of Vehicle, Internet of Vehicles, Internet of Vehicles intrusion detection system, Intrusion detection, Intrusion Detection System (IDS), Kernel, low-powered embedded vehicle terminal, Metrics, network attacks, packet header analysis, pubcrawl, Resiliency, security issues, telecommunication traffic, vehicular ad hoc networks, Wireless sensor networks |
| Abstract | With the continuous development of the Internet of Vehicles, vehicles are no longer isolated nodes, but become a node in the car network. The open Internet will introduce traditional security issues into the Internet of Things. In order to ensure the safety of the networked cars, we hope to set up an intrusion detection system (IDS) on the vehicle terminal to detect and intercept network attacks. In our work, we designed an intrusion detection system for the Internet of Vehicles based on a convolutional neural network, which can run in a low-powered embedded vehicle terminal to monitor the data in the car network in real time. Moreover, for the case of packet encryption in some car networks, we have also designed a separate version for intrusion detection by analyzing the packet header. Experiments have shown that our system can guarantee high accuracy detection at low latency for attack traffic. |
| DOI | 10.1109/ISPA-BDCloud-SustainCom-SocialCom48970.2019.00234 |
| Citation Key | peng_internet_2019 |
An Internet of Vehicles Intrusion Detection System Based on a Convolutional Neural Network