TY - GEN
T1 - Intrusion Detection in Autonomous Vehicular Networks
T2 - 2019 INFOCOM IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2019
AU - Xing, Rui
AU - Su, Zhou
AU - Wang, Yuntao
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/4
Y1 - 2019/4
N2 - The ever-growing development of autonomous driving vehicles (ADVs) attracts a lot of attention of the intelligent industry. The security issues of the autonomous vehicular networks become essential since the automated components onboard are easily attacked which may leads ADVs malfunction. However, few of the intrusion detection systems (IDSs) used for mitigating above issues consider the intrusion detection in autonomous vehicular networks (AVNs). Therefore, in this paper, we propose a novel intrusion detection scheme for AVNs. Firstly, we establish the trust assessment model for ADVs including direct assessment and indirect assessment. Then, we present the intrusion detection model by confidence assessment to intrusion reports. Next, a Q-learning based incentive model is given to intelligently stimulate ADVs to participate the intrusion reporting. Finally, the simulation results show the efficiency of our proposal compared with conventional schemes in detection rate.
AB - The ever-growing development of autonomous driving vehicles (ADVs) attracts a lot of attention of the intelligent industry. The security issues of the autonomous vehicular networks become essential since the automated components onboard are easily attacked which may leads ADVs malfunction. However, few of the intrusion detection systems (IDSs) used for mitigating above issues consider the intrusion detection in autonomous vehicular networks (AVNs). Therefore, in this paper, we propose a novel intrusion detection scheme for AVNs. Firstly, we establish the trust assessment model for ADVs including direct assessment and indirect assessment. Then, we present the intrusion detection model by confidence assessment to intrusion reports. Next, a Q-learning based incentive model is given to intelligently stimulate ADVs to participate the intrusion reporting. Finally, the simulation results show the efficiency of our proposal compared with conventional schemes in detection rate.
KW - Autonomous vehicular networks
KW - Intrusion detection
KW - Q-learning
KW - trust assessment
UR - https://www.scopus.com/pages/publications/85073226436
U2 - 10.1109/INFCOMW.2019.8845219
DO - 10.1109/INFCOMW.2019.8845219
M3 - 会议稿件
AN - SCOPUS:85073226436
T3 - INFOCOM 2019 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2019
SP - 79
EP - 83
BT - INFOCOM 2019 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 29 April 2019 through 2 May 2019
ER -