Research on Information Security Protection Strategy Optimization for Intelligent Connected Vehicles

被引:0
作者
Ren, Yilong [1 ,2 ]
Zhao, Fuxia [3 ]
Xu, Chi [3 ]
Yu, Haiyang [1 ,2 ]
机构
[1] Beihang Univ, Beijing Adv Innovat Ctr Big Data & Brain Comp, Sch Transportat Sci & Engn, Beijing, Peoples R China
[2] Beijing Key Lab Vehicle Rd Coordinat & Safety Con, Beijing, Peoples R China
[3] Beihang Univ, Sch Transportat Sci & Engn, Beijing, Peoples R China
来源
CICTP 2021: ADVANCED TRANSPORTATION, ENHANCED CONNECTION | 2021年
关键词
Intelligent Connected Vehicles; Information Security; Protection Strategy; Model Optimization; CYBER SECURITY;
D O I
暂无
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
In recent years, the functions of intelligent connected vehicles are becoming more and more powerful, which increases the number of electronic control units (ECUs) and the number of external connections, etc. However, these technologies made the intelligent connected vehicles more vulnerable to attack. Intelligent connected vehicles have been attacked frequently, causing serious threats to life and property. Therefore, intelligent connected vehicles need to carry out information security protection. In this paper, cost, delay and information security factors are considered, and according to the characteristics of the attack path of intelligent connected vehicles, an information security protection strategy for intelligent connected vehicles is proposed. The protection strategy improves the efficiency of information security measures allocation and reduces the cost.
引用
收藏
页码:1587 / 1596
页数:10
相关论文
共 17 条
[1]   Anomaly Detection System for Altered Signal Values within the Intra-Vehicle Network [J].
Abbas, Mohamed ;
Safar, Mona ;
Salem, Ashraf .
2020 15TH IEEE INTERNATIONAL CONFERENCE ON DESIGN & TECHNOLOGY OF INTEGRATED SYSTEMS IN NANOSCALE ERA (DTIS 2020), 2020,
[2]  
Bharati S, 2020, ARXIV PRINTS
[3]   Real-Time Detection and Estimation of Denial of Service Attack in Connected Vehicle Systems [J].
Biron, Zoleikha Abdollahi ;
Dey, Satadru ;
Pisu, Pierluigi .
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2018, 19 (12) :3893-3902
[4]  
Dibaei M., 2019, IEEE J
[5]  
He K, 2020, 2020 INT C COMP INF
[6]   A Novel Intrusion Detection Method Using Deep Neural Network for In-Vehicle Network Security [J].
Kang, Min-Ju ;
Kang, Je-Won .
2016 IEEE 83RD VEHICULAR TECHNOLOGY CONFERENCE (VTC SPRING), 2016,
[7]  
Kawanishi Y, 2019, SECUR COMMUN NETW, V2019, P1
[8]   Security risk assessment framework for smart car using the attack tree analysis [J].
Kong, Hee-Kyung ;
Hong, Myoung Ki ;
Kim, Tae-Sung .
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING, 2018, 9 (03) :531-551
[9]   Network and System Level Security in Connected Vehicle Applications [J].
Liang, Hengyi ;
Jagielski, Matthew ;
Zheng, Bowen ;
Lin, Chung-Wei ;
Kang, Eunsuk ;
Shiraishi, Shinichi ;
Nita-Rotaru, Cristina ;
Zhu, Qi .
2018 IEEE/ACM INTERNATIONAL CONFERENCE ON COMPUTER-AIDED DESIGN (ICCAD) DIGEST OF TECHNICAL PAPERS, 2018,
[10]  
Macher G, 2016, INT C COMP SAF SEC