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; 张海璐; 刘楚环; 肖亮; 朱珍民

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

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基于强化学习的低时延车联网群密钥分配管理技术

学术论文 | 更新时间：2024-06-03

- 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
- 网络与信息安全学报 2020年6卷第5期页码：119-125
- 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：
  
  [ "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" ]
- 基金信息：
  
  国家自然科学基金;The National Natural Science Foundation of China(61971366)
- DOI：10.11959/j.issn.2096-109x.2020055
  中图分类号： TN97
- 网络首发：2020-10，
  
  纸质出版：2020-10-15
- 稿件说明：
移动端阅览
徐堂炜, 张海璐, 刘楚环, 等. 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, 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.
徐堂炜, 张海璐, 刘楚环, 等. 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. DOI： 10.11959/j.issn.2096-109x.2020055.

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

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