Detection method of physical-layer impersonation attack based on deep Q-network in edge computing

doi:10.11772/j.issn.1001-9081.2020020179

Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (11): 3229-3235.DOI: 10.11772/j.issn.1001-9081.2020020179

• Cyber security • Previous Articles Next Articles

Detection method of physical-layer impersonation attack based on deep Q-network in edge computing

YANG Jianxi¹, ZHANG Yuanli², JIANG Hua¹, ZHU Xiaochen²

1. Department of Communication Engineering, Beijing Electronic Science and Technology Institute, Beijing 100070, China;
2. College of Communication Engineering, Xidian University, Xi'an Shaanxi 710071, China

Received:2020-02-24 Revised:2020-05-25 Online:2020-06-05 Published:2020-11-10
Supported by:
This work is partially supported by the Fundamental Research Funds for the Central Universities (328201912), the Open Project Fund of Key Laboratory of Network Assessment Technology of Chinese Academy of Sciences (KFKT2019-006).

边缘计算中基于深度Q网络的物理层假冒攻击检测方法

杨建喜¹, 张媛利², 蒋华¹, 朱晓辰²

1. 北京电子科技学院通信工程系, 北京 100070;
2. 西安电子科技大学通信工程学院, 西安 710071

通讯作者: 张媛利(1995-),女,陕西咸阳人,硕士研究生,主要研究方向:深度学习、物联网安全;13772186903@163.com
作者简介:杨建喜(1973-),男,山西原平人,副教授,博士,主要研究方向:无线网络安全、通信网络信号抗干扰处理;蒋华(1962-),男,山西大同人,教授,硕士,主要研究方向:通信与信息安全;朱晓辰(1996-),女,河南平顶山人,硕士研究生,主要研究方向:物联网安全
基金资助:
中央高校基本科研业务费资助项目（328201912）；中国科学院网络测评技术重点实验室开放课题基金资助项目（KFKT2019-006）。

Abstract

Abstract: In the edge computing, the communication between edge computing nodes and terminal devices is vulnerable to impersonation attacks, therefore a physical-layer impersonation attack detection algorithm based on Deep Q-Network (DQN) was proposed. Firstly, an impersonation attack model was built in the edge computing network, a hypothesis test based on the physical-layer Channel State Information (CSI) was established by the receiver, and the Euclidean distance between the currently measured CSI and the last recorded CSI was taken as the test statistics. Secondly, for the dynamic environment of edge computing, the DQN algorithm was used to adaptively select the optimal test threshold with the goal of maximizing the gain of the receiver. Finally, whether the current sender was an impersonation attacker was determined by comparing the statistics with the test threshold. The simulation results show that the Signal-to-Interference plus Noise Ratio (SINR) and channel gain ratio have certain effect on the performance of the detection algorithm, but when the relative change of channel gain is lower than 0.2, the false alarm rate, miss rate and average error rate of the algorithm are less than 5%. Therefore, the detection algorithm is adaptive to the dynamical environment of edge computing.

Key words: edge computing, impersonation attack, physical layer security, Deep Q-Network (DQN), hypothesis test

摘要： 针对边缘计算中边缘计算节点与终端设备之间的通信容易受到假冒攻击的安全问题，提出了一种基于深度Q网络（DQN）的物理层假冒攻击检测算法。首先，构建边缘计算网络中的假冒攻击模型，接收端基于物理层信道状态信息（CSI）建立假设检验，并将当前测量得到的CSI和上次记录的CSI之间的欧氏距离作为检验统计量；其次，利用DQN算法以接收端回报最大化为目标自适应地为边缘计算动态网络环境筛选出当前最优检测阈值；最后，通过对比检验统计量与检测阈值来判断当前发送端是否为假冒攻击者。仿真结果表明，信干噪比（SINR）和信道增益比对检测算法性能有一定的影响，但在信道增益相对变化不超过0.2时，误报率、漏检率和平均错误率低于5%，因此该检测算法能够自适应边缘计算动态变化的网络环境。

关键词: 边缘计算, 假冒攻击, 物理层安全, 深度Q网络, 假设检验

CLC Number:

TP393.08

YANG Jianxi, ZHANG Yuanli, JIANG Hua, ZHU Xiaochen. Detection method of physical-layer impersonation attack based on deep Q-network in edge computing[J]. Journal of Computer Applications, 2020, 40(11): 3229-3235.

杨建喜, 张媛利, 蒋华, 朱晓辰. 边缘计算中基于深度Q网络的物理层假冒攻击检测方法[J]. 计算机应用, 2020, 40(11): 3229-3235.

References

[1] KHAN W Z,AHMED E,HAKAK S,et al. Edgecomputing:a survey[J]. Future Generation Computer Systems, 2019, 97:219-235.
[2] 高志鹏,尧聪聪,肖楷乐. 移动边缘计算:架构、应用和挑战[J]. 中兴通讯技术,2019, 25(3):23-30.(GAO Z P,YAO C C, XIAO K L. Mobile edgecomputing:architecture,applications and challenges[J]. ZTE Communications,2019, 25(3):23-30.)
[3] SHI W, DUSTDAR S. The promise of edgecomputing[J]. Computer,2016,49(5):78-81.
[4] WANG T, ZHANG G, LIU A, et al. A secure IoT service architecture with an efficient balance dynamics based on cloud and edgecomputing[J]. IEEE Internet of Things Journal,2019,6(3):4831-4843.
[5] LIAO R,WEN H,WU J,et al. Security enhancement for mobile edgecomputing through physical layer authentication[J]. IEEE Access,2019,7:116390-116401.
[6] LIU J, XIAO L, LIU G, et al. Active authentication with reinforcement learning based on ambient radio signals[J]. Multimedia Tools and Applications,2017,76(3):3979-3998.
[7] CONLEY W G, MILLER A J. Cognitive jamming game for dynamically countering ad hoc cognitive radio networks[C]//Proceedings of the 2013 IEEE Military Communications Conference. Piscataway:IEEE,2013:1176-1182.
[8] 刘全, 翟建伟, 章宗长, 等. 深度强化学习综述[J]. 计算机学报, 2018,41(1):1-27. (LIU Q,ZHAI J W,ZHANG Z Z,et al. A survey on deep reinforcement learning[J]. Chinese Journal of Computers,2018,41(1):1-27.)
[9] ROMAN R,LOPEZ J,MAMBO M. Mobile edgecomputing,fog et al.:a survey and analysis of security threats and challenges[J]. Future Generation Computer Systems,2018,78(Pt 2):680-698.
[10] ATAT R, LIU L, CHEN H, et al. Enabling cyber-physicalcommunication in 5G cellular networks:challenges, spatial spectrum sensing,and cyber-security[J]. IET Cyber-Physical Systems Theory and Applications,2017,2(1):49-54.
[11] IBRAHIM M H. Octopus:an edge-fog mutual authentication scheme[J]. International Journal of Network Security,2016,18(6):1089-1101.
[12] BEN AMOR A,ABID M,MEDDEB A. A privacy-preserving authentication scheme in an edge-fog environment[C]//Proceedings of the 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications. Piscataway:IEEE,2017:1225-1231.
[13] DAO N N,VU D N,LEE Y,et al. MAEC-X:DDoS prevention leveraging multi-access edgecomputing[C]//Proceedings of the 2018 International Conference on Information Networking. Piscataway:IEEE,2018:245-248.
[14] KHALIL I,KHREISHAH A,AZEEM M. Consolidated identity management system for secure mobile cloudcomputing[J]. Computer Networks,2014,65:99-110.
[15] XIAO L, GREENSTEIN L, MANDAYAM N B, et al. Fingerprints in the Ether:using the physical layer for wireless authentication[C]//Proceedings of the 2007 IEEE International Conference on Communications. Piscataway:IEEE,2007:4646-4651.
[16] 李古月. 无线通信物理层安全理论与方法研究[D]. 南京:东南大学,2017:31-34. (LI G Y. Research on theory and method of wirelesscommunication physical layer security[D]. Nanjing:Southeast University,2017:31-34.)
[17] LIU R,TRAPPE W. Securing Wireless Communications at the Physical Layer[M]. Boston:Springer,2010:241-242.
[18] RSUTTON R S, BARTO A G. Reinforcement Learning:an Introduction[M]. Cambridge:MIT Press,1998:665-685.
[19] KULKARNI P. Reinforcement and deep reinforcement machine learning[M]. Cham:Springer,2017:59-83.
[20] MA Z,LEIJON A. Bayesian estimation of beta mixture models with variational inference[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2011,33(11):2160.
[21] XIAO L,GREENSTEIN L J,MANDAYAM N B,et al. Channelbased spoofing detection in frequency-selective Rayleigh channels[J]. IEEE Transactions on Wireless Communications,2009,8(12):5948-5956.
[22] XIAO L,GREENSTEIN L J,MANDAYAM N B,et al. Using the physical layer for wireless authentication in time-variant channels[J]. IEEE Transactions on Wireless Communications,2008,7(7):2571-2579.
[23] TU S,WAQAS M,UR REHMAN S,et al. Security in fogcomputing:a novel technique to tackle an impersonation attack[J]. IEEE Access,2018,6:74993-75001.

Detection method of physical-layer impersonation attack based on deep Q-network in edge computing

边缘计算中基于深度Q网络的物理层假冒攻击检测方法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

[1]	Qinzhuang ZHAO, Hongye TAN. Time series causal inference method based on adaptive threshold learning [J]. Journal of Computer Applications, 2024, 44(9): 2660-2666.
[2]	Guiyong LI, Bin HE, Lei FANG. Key generation algorithm based on cyclic grouping of difference thresholds [J]. Journal of Computer Applications, 2024, 44(7): 2110-2115.
[3]	Junna ZHANG, Xinxin WANG, Tianze LI, Xiaoyan ZHAO, Peiyan YUAN. Task offloading method based on dynamic service cache assistance [J]. Journal of Computer Applications, 2024, 44(5): 1493-1500.
[4]	Xiaoyan ZHAO, Wei HAN, Junna ZHANG, Peiyan YUAN. Collaborative offloading strategy in internet of vehicles based on asynchronous deep reinforcement learning [J]. Journal of Computer Applications, 2024, 44(5): 1501-1510.
[5]	Ming ZHANG, Le FU, Haifeng WANG. Relay control model for concurrent data flow in edge computing [J]. Journal of Computer Applications, 2024, 44(12): 3876-3883.
[6]	Yan SHI, Yue WU, Dongqing ZHAO. Hybrid intelligent reflecting surface and relay assisted secure transmission scheme based on cooperative interference [J]. Journal of Computer Applications, 2024, 44(12): 3893-3898.
[7]	Xuyan ZHAO, Yunhe CUI, Chaohui JIANG, Qing QIAN, Guowei SHEN, Chun GUO, Xianchao LI. CHAIN： edge computing node placement algorithm based on overlapping domination [J]. Journal of Computer Applications, 2023, 43(9): 2812-2818.
[8]	Wanzhen CHEN, En ZHANG, Leiyong QIN, Shuangxi HONG. Privacy-preserving federated learning algorithm based on blockchain in edge computing [J]. Journal of Computer Applications, 2023, 43(7): 2209-2216.
[9]	Xiaolin LI, Yusang JIANG. Task offloading algorithm for UAV-assisted mobile edge computing [J]. Journal of Computer Applications, 2023, 43(6): 1893-1899.
[10]	Tengfei CAO, Yanliang LIU, Xiaoying WANG. Edge computing and service offloading algorithm based on improved deep reinforcement learning [J]. Journal of Computer Applications, 2023, 43(5): 1543-1550.
[11]	Zhe WANG, Qiming WANG, Taoshen LI, Lina GE. Joint optimization method for SWIPT edge network based on deep reinforcement learning [J]. Journal of Computer Applications, 2023, 43(11): 3540-3550.
[12]	YAO Huayong, YE Dongyi, CHEN Zhaojiong. Multi-round conversational reinforcement learning recommendation algorithm via multi-granularity feedback [J]. Journal of Computer Applications, 2023, 43(1): 15-21.
[13]	Yanpei LIU, Ningning CHEN, Yunjing ZHU, Liping WANG. Review of mobile edge caching optimization technologies for 5G/Beyond 5G [J]. Journal of Computer Applications, 2022, 42(8): 2487-2500.
[14]	Jieqin WANG, Shihyang LIN, Shiming PENG, Shuo JIA, Miaohui YANG. Hierarchical resource allocation mechanism of cooperative mobile edge computing [J]. Journal of Computer Applications, 2022, 42(8): 2501-2510.
[15]	Huiwen XIA, Zhongyu ZHAO, Zhuoer WANG, Qingyong ZHANG, Feng PENG. Public transportation epidemic monitoring system based on edge computing [J]. Journal of Computer Applications, 2022, 42(7): 2132-2138.