基于强化学习的低时延车联网群密钥分配管理技术

doi:10.11959/j.issn.2096-109x.2020055

网络与信息安全学报 ›› 2020, Vol. 6 ›› Issue (5): 119-125.doi: 10.11959/j.issn.2096-109x.2020055

基于强化学习的低时延车联网群密钥分配管理技术

徐堂炜¹,张海璐¹,刘楚环¹,肖亮¹(),朱珍民²

¹ 厦门大学信息学院，福建厦门 361005
² 中国科学院大学计算技术研究所，北京 100190

修回日期:2020-03-19 出版日期:2020-10-15 发布日期:2020-10-19
作者简介:徐堂炜（1995- ），男，福建三明人，厦门大学硕士生，主要研究方向为车联网、强化学习|张海璐（1996- ），女，福建龙岩人，厦门大学硕士生，主要研究方向为无线通信、网络安全|刘楚环（1993- ），男，安徽宿州人，厦门大学博士生，主要研究方向为无线通信、车联网网络安全|肖亮（1980- ），女，河北石家庄人，厦门大学教授、博士生导师，主要研究方向为网络安全、通信与网络|朱珍民（1962- ），男，湖南慈利人，中国科学院大学教授，主要研究方向为普适计算技术、嵌入式技术、多媒体技术、动力学系统实时数值仿真算法
基金资助:
国家自然科学基金(61971366)

Reinforcement learning based group key agreement scheme with reduced latency for VANET

Tangwei1 XU¹,Hailu ZHANG¹,Chuhuan LIU¹,Liang XIAO¹(),Zhenmin ZHU²

¹ School of Informatics,Xiamen University,Xiamen 361005,China
² Institute of Computing technology,Chinese Academy of Sciences University,Beijing 100190,China

Revised:2020-03-19 Online:2020-10-15 Published:2020-10-19
Supported by:
The National Natural Science Foundation of China(61971366)

摘要/Abstract

摘要：

车联网的通信密钥管理水平决定其信息保密性和抵御电子欺骗等基于用户身份的攻击的安全性。现有密钥管理方案采用固定频率更新车联网的群密钥，增大了密钥更新时延和加密时延。提出一种基于强化学习的低时延车联网群密钥分配管理技术，令群首车辆根据周边车辆数目变化和密钥更新记录等因素优化其群密钥的更新频率和密钥长度。该技术不需要群首车辆预知附近的车流变化模型与访问驱动高速缓存攻击模型，降低加解密时延，并在基于高级加密标准的车联网仿真进行验证。仿真结果表明，相比基准的群密钥管理方案，该技术降低密钥更新的传输时延，减少车联网的加解密计算时延，并提高群密钥的保密性。

关键词: 车联网, 密钥管理, 强化学习

Abstract:

Key agreement scheme of vehicle ad hoc networks (VANET) determines the privacy and security against user identity-based attacks,such as spoofing attack.The existing work updates the group shared key at a fixed frequency,which increases the processing latency of the key updates and the encryption and decryption of group communication.A reinforcement learning based dynamic key agreement scheme was proposed to enable the group cluster head to optimize the key update frequency and key length.This scheme optimizes the calculation latency of encryption and decryption based on the number of surrounding vehicles,the previous key update records without the knowledge of the traffic flow model and the access-driven cache attack model.Simulation results show that this scheme reduces the transmission delay of the communication overhead and the computational latency of encryption and decryption,and improves the security of the group key compared with the benchmark scheme.

Key words: VANET, group key agreement, reinforcement learning

中图分类号:

TN97

徐堂炜,张海璐,刘楚环,肖亮,朱珍民. 基于强化学习的低时延车联网群密钥分配管理技术[J]. 网络与信息安全学报, 2020, 6(5): 119-125.

Tangwei1 XU,Hailu ZHANG,Chuhuan LIU,Liang XIAO,Zhenmin ZHU. Reinforcement learning based group key agreement scheme with reduced latency for VANET[J]. Chinese Journal of Network and Information Security, 2020, 6(5): 119-125.

图/表 4

表1

重要参数符号　Table 1 Notations of important parameters"

参数	含义
M^k	k时刻群集内车辆数目
vk	群集车辆平均速度
g^k	前D个时刻群集密钥更新决策
ρ^k	群集通信安全等级
$x_{1}^{k}$	更新密钥决策
$x_{2}^{k}$	更新密钥长度
λ	泊松分布均值
VPK/VSK	车辆的公钥/私钥
RPK/RSK	RSU的公钥/私钥
n	TA选择的随机质数
E(·)	加密
h(·)	Hash函数
T/T*	发送/接收时间戳

表1

图1

图2

图3

参考文献 24

[1]	刘俊杰, 赵佳, 张强 ,等. 军事车载网密钥管理方案研究[J]. 网络与信息安全学报, 2018,4(8): 39-46.
	LIU J J , ZHAO J , ZHANG Q ,et al. Research on key management scheme for military vehicle network[J]. Chinese Journal of Network and Information Security, 2018,4(8): 39-46.
[2]	LU Z J , QU G , LIU Z L . A survey on recent advances in vehicular network security,trust,and privacy[J]. IEEE Transactions on Intelligent Transportation Systems, 2018,20(2): 760-776.
[3]	王良民, 李婷婷 . 基于车辆身份的车联网结构与安全[J]. 网络与信息安全学报, 2016,2(2): 41-54.
	WANG L M , LI T T . Structure and security of vehicle network based on vehicle identity[J]. Chinese Journal of Network and Information Security, 2016,2(2): 41-54.
[4]	GULLASCH D , BANGERTER E , KRENN S . Cache gamesbringing access-based cache attacks on AES to practice[C]// IEEE Symposium on Security and Privacy. 2011: 490-505.
[5]	LAI C Z , ZHOU H B , CHENG N ,et al. Secure group communications in vehicular networks:a software-defined network-enabled architecture and solution[J]. IEEE Vehicular Technology Magazine, 2017,12(4): 40-49.
[6]	RAYA M , HUBAUX J P . Securing vehicular Ad Hoc networks[J]. Journal of Computer Security, 2007,15(1): 39-68.
[7]	YEH L Y , HUANG Y L , JOSEPH A D ,et al. A batch-authenticated and key agreement framework for P2P-based online social networks[J]. IEEE Transactions on Vehicular Technology, 2012,61(4): 1907-1924.
[8]	ISLAM S K H , OBAIDAT M S , VIJAYAKUMAR P ,et al. A robust and efficient password-based conditional privacy preserving authentication and group-key agreement protocol for VANETs[J]. Future Generation Computer Systems, 2018,84(7): 216-227.
[9]	JIANG S R , ZHU X Y , WANG L M . An efficient anonymous batch authentication scheme based on HMAC for VANETs[J]. IEEE Transactions on Intelligent Transportation Systems, 2016,17(8): 2193-2204.
[10]	LIU Y B , WANG Y H , CHANG G H . Efficient privacy-preserving dual authentication and key agreement scheme for secure V2V communications in an IoV paradigm[J]. IEEE Transactions on Intelligent Transportation Systems, 2017,18(10): 2740-2749.
[11]	XIAO L , ZHUANG W H , ZHOU S ,et al. Learning-based VANET communication and security techniques[M]. Berlin: SpringerPress, 2019.
[12]	LUONG N C , HOANG D T , GONG S ,et al. Applications of deep reinforcement learning in communications and networking:a survey[J]. IEEE Communications Surveys ＆ Tutorials, 2019,21(4): 3133-3174.
[13]	XIAO L , LU X Z , XU D J ,et al. UAV relay in VANETs against smart jamming with reinforcement learning[J]. IEEE Transactions on Vehicular Technology, 2018,67(5): 4087-4097.
[14]	XIAO L , JIANG D H , XU D J ,et al. Two-dimensional anti-jamming mobile communication based on reinforcement learning[J]. IEEE Transactions on Vehicular Technology, 2018,67(10): 9499-9512.
[15]	XIAO L , WAN X Y , HAN Z . PHY-layer authentication with multiple landmarks with reduced overhead[J]. IEEE Transactions on Wireless Communications, 2017,17(3): 1676-1687.
[16]	XIAO L , CHEN T H , HAN G A ,et al. Game theoretic study on channel-based authentication in MIMO systems[J]. IEEE Transactions on Vehicular Technology, 2017,66(8): 7474-7484.
[17]	CHU M , LI H , LIAO X W ,et al. Reinforcement learning-based multiaccess control and battery prediction with energy harvesting in IoT systems[J]. IEEE Internet of Things Journal, 2018,6(2): 2009-2020.
[18]	HE Y , ZHANG Z , YU F R ,et al. Deep-reinforcement learning based optimization for cache enabled opportunistic interference alignment wireless networks[J]. IEEE Transactions on Vehicular Technology, 2017,66(11): 10433-10445.
[19]	JIANG D , TALIWAL V , MEIER A ,et al. Design of 5.9 GHz DSRC-based vehicular safety communication[J]. IEEE Wireless Communications, 2006,13(5): 36-43.
[20]	REN M , KHOUKHI L , LABIOD H ,et al. A mobility-based scheme for dynamic clustering in vehicular Ad-Hoc networks (VANETs)[J]. Vehicular Communications, 2017,9(3): 233-241.
[21]	CUI J , TAO X , ZHANG J ,et al. HCPA-GKA:a hash function-based conditional privacy-preserving authentication and group-key agreement scheme for VANETs[J]. Vehicular Communications, 2018,14(3): 15-25.
[22]	XIAO L , LI Y , HAN G ,et al. PHY-layer spoofing detection with reinforcement learning in wireless networks[J]. IEEE Transactions on Vehicular Technology, 2016,65(12): 10037-10047.
[23]	CHEN C , XIAO T T , ZHAO H H ,et al. GAS:a group acknowledgment strategy for popular content distribution in Internet of vehicle[J]. Vehicular Communications, 2019,17(3): 35-49.
[24]	ZAIDI K , MILOJEVIC M B , RAKOCEVIC V ,et al. Host-based intrusion detection for VANETs:a statistical approach to rogue node detection[J]. IEEE Transactions on Vehicular Technology, 2015,65(8): 6703-6714.

基于强化学习的低时延车联网群密钥分配管理技术

Reinforcement learning based group key agreement scheme with reduced latency for VANET

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