计算机工程 ›› 2020, Vol. 46 ›› Issue (1): 38-44.doi: 10.19678/j.issn.1000-3428.0053943

• 热点与综述 • 上一篇    下一篇

移动雾计算中基于强化学习的伪装攻击检测算法

于金亮1, 涂山山1,2, 孟远1,2   

  1. 1. 北京工业大学 信息学部, 北京 100124;
    2. 可信计算北京市重点实验室, 北京 100124
  • 收稿日期:2019-02-19 修回日期:2019-05-02 出版日期:2020-01-15 发布日期:2019-07-12
  • 作者简介:于金亮(1996-),男,硕士研究生,主研方向为雾计算、机器学习、信息安全;涂山山(通信作者),讲师;孟远,硕士研究生。
  • 基金项目:
    国家自然科学基金(61801008);国家重点研发计划(2018YFB0803600);北京市自然科学基金(L172049);北京市教委科研计划(KM201910005025)。

Impersonation Attack Detection Algorithm Based on Reinforcement Learning in Mobile Fog Computing

YU Jinliang1, TU Shanshan1,2, MENG Yuan1,2   

  1. 1. Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China;
    2. Beijing Key Laboratory of Trusted Computing, Beijing 100124, China
  • Received:2019-02-19 Revised:2019-05-02 Online:2020-01-15 Published:2019-07-12

摘要: 在移动雾计算中,雾节点与移动终端用户之间的通信容易受到伪装攻击,从而带来通信和数据传输的安全问题。基于移动雾环境下的物理层密钥生成策略,提出一种基于强化学习的伪装攻击检测算法。构建移动雾计算中的伪装攻击模型,在该模型下设计基于Q-学习算法的伪装攻击检测算法,实现在动态环境下对伪装攻击的检测,在此基础上,分析密钥生成策略在假设检验中的漏报率、误报率和平均错误率以检验算法性能。实验结果表明,该算法能够在动态环境中有效地防范伪装攻击,可使检测性能迅速收敛并达到稳定,且具有较低的平均检测错误率。

关键词: Q-学习算法, 物理层安全, 伪装攻击, 物理层密钥生成, 假设检验

Abstract: In mobile fog computing,the communication between fog nodes and mobile end users is vulnerable to impersonation attacks,thus causing security issues in communication and data transmission.On the basis of the physical layer key generation strategy in mobile fog environment,this paper proposes an impersonation attack detection method based on reinforcement learning.The impersonation attack model in fog computing is constructed and the impersonation attack detection algorithm based on Q-learning algorithm is designed under this model,so as to detect impersonation attacks in a dynamic environment.On this basis,this paper analyzes the False Alarm Rate(FAR),Miss Detection Rate(MDR) and Average Error Rate(AER) of this strategy in the hypothesis testing,so as to judge the performance of the algorithm.Experimental results show that the proposed algorithm can effectively prevent impersonation attacks in a dynamic environment and its detection performance can converge rapidly and reach a stable state.Besides,the proposed algorithm has higher detection accuracy and lower average detection error rate.

Key words: Q-learning algorithm, physical layer security, impersonation attack, physical layer key generation, hypothesis testing

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