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

Tangwei1 XU; Hailu ZHANG; Chuhuan LIU; Liang XIAO; Zhenmin ZHU

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

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

Papers | 更新时间：2024-06-03

- Reinforcement learning based group key agreement scheme with reduced latency for VANET
- Chinese Journal of Network and Information Security Vol. 6, Issue 5, Pages: 119-125(2020)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-109x.2020055
  CLC： TN97
- Online First：2020-10，
  
  Published：15 October 2020
- 稿件说明：
移动端阅览
Tangwei1 XU, Hailu ZHANG, Chuhuan LIU, et al. 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.
DOI：

Tangwei1 XU, Hailu ZHANG, Chuhuan LIU, et al. 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. DOI： 10.11959/j.issn.2096-109x.2020055.

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。

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.

关键词

Keywords

references

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 .

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 .

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 .

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 .

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 .

RAYA M , HUBAUX J P . Securing vehicular Ad Hoc networks [J ] . Journal of Computer Security , 2007 , 15 ( 1 ): 39 - 68 .

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 .

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 .

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 .

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 .

XIAO L , ZHUANG W H , ZHOU S , et al . Learning-based VANET communication and security techniques [M ] . Berlin : SpringerPress , 2019 .

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 .

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 .

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 .

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 .

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 .

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 .

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 .

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 .

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 .

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 .

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 .

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 .

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 .

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