基于辅助信息补偿和控制信号编码的重放攻击检测方法

张正道; 杨佳佳; 谢林柏

doi:10.16383/j.aas.c210092

基于辅助信息补偿和控制信号编码的重放攻击检测方法

doi: 10.16383/j.aas.c210092

张正道^{1, 2,},
杨佳佳¹,
谢林柏^{1, 2,}

1.
江南大学物联网工程学院无锡 214122
2.
江南大学教育部物联网应用技术中心无锡 214122

基金项目: 国家重点研发计划课题(2018YFB1701903)资助

详细信息

作者简介:
张正道：博士, 江南大学物联网工程学院副教授. 主要研究方向为信息物理系统安全性, 系统状态监测与故障诊断. 本文通信作者. E-mail: wxzzd@jiangnan.edu.cn

杨佳佳：江南大学物联网工程学院硕士研究生. 主要研究方向为信息物理系统的攻击检测. E-mail: 6181905010@stu.jiangnan.edu.cn

谢林柏：博士, 江南大学物联网工程学院教授. 主要研究方向为过程建模与控制, 智能检测与系统安全性. E-mail: xie_linbo@jiangnan.edu.cn

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出版历程
- 收稿日期: 2021-01-28
- 网络出版日期: 2021-07-06
- 刊出日期: 2023-07-20

Replay Attack Detection Method Based on Auxiliary Information Compensation and Control Signal Coding

ZHANG Zheng-Dao^{1, 2
,},
YANG Jia-Jia¹,
XIE Lin-Bo^{1, 2
,}

1.
College of Internet of Things Engineering, Jiangnan University, Wuxi 214122
2.
Engineering Research Center of Internet of Things Applied Technology, Ministry of Education, Jiangnan University, Wuxi 214122

Funds: Supported by National Key Research and Development Program of China (2018YFB1701903)

More Information

Author Bio:
ZHANG Zheng-Dao　Ph.D., associate professor at the College of Internet of Things Engineering, Jiangnan University. His research interest covers security of cyber physical system, state monitoring, and fault diagnosis. Corresponding author of this paper

YANG Jia-Jia　Master student at the College of Internet of Things Engineering, Jiangnan University. Her main research interest is attack detection in cyber physical systems

XIE Lin-Bo　Ph.D., professor at the College of Internet of Things Engineering, Jiangnan University. His research interest covers process modeling and control, intelligent detection, and system safety

摘要

摘要: 向最优控制信号中加入编码信号是实现信息物理系统(Cyber physical system, CPS)重放攻击检测的有效方法, 但会造成系统控制性能的损失. 如何在保证重放攻击检测率条件下降低系统的控制性能损失是一个值得研究的问题. 为此, 提出一种基于辅助信息补偿的控制信号编码检测方法, 通过向测量值添加辅助信号补偿控制编码信号对最优状态估计的影响. 首先, 证明了此方案下重放攻击的可检测性, 导出检测率的上界与检测函数阈值间的定量关系. 其次, 证明了加入辅助信号后系统控制信号与未添加编码信息时相同, 之前时刻的控制编码信号不会造成累积效应. 因此, 系统当前时刻的控制性能损失仅与当前时刻编码信号的大小有关. 最后, 将编码信号的协方差矩阵、检测率和检测阈值之间的关系表示成一个最优化问题, 给出了编码信号方差的计算方法. 仿真结果表明, 本文方法能有效地检测重放攻击的发生, 且系统控制性能的损失较小.
- 信息物理系统 /
- 重放攻击 /
- 攻击检测 /
- 控制信号编码
Abstract: Adding coding signal to the optimal control signal is an effective method to detect the replay attack in cyber physical systems (CPSs), which always causes loss of system control performance. How to reduce the sacrifice of system control performance and guarantee the detection rate of replay attack is a valuable problem to be studied. In this paper, a control signal coding detection scheme has been proposed based on auxiliary information compensation. By adding auxiliary signal to the system measurements, the influence of the coding signal on the optimal state estimation has been compensated. Firstly, the detectability of replay attack under the proposed scheme is proved, and the quantitative relationship between the upper bound of the detection rate and the threshold of the detection function is derived. Secondly, it is demonstrated that the control signal generated by the controller after adding auxiliary signal is the same as the control signal without coding signal. Therefore, the historical coding signal does not effect the system cumulatively. The loss of system control performance at current moment is only related to the size of the current coding signal. Finally, the relationship between the covariance matrix of the coding signal, the detection rate and the detection threshold is expressed as an optimization problem, through which the variance of the coding signal can be derived. Simulation results show that the attack can be effectively detected throught the proposed scheme, while the loss in the corresponding system control performance is relatively smaller.
- Cyber physical system (CPS) /
- replay attack /
- attack detection /
- control signal coding

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图 1 本文所提方法的系统框图

Fig. 1 System diagram of the proposed scheme in this paper

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图 2 直流电机系统正常运行时的检测函数曲线

Fig. 2 The detection function curve of the normal DC motor system

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图 3 攻击场景1下的检测函数曲线

Fig. 3 The detection function curve under attack scenario 1

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图 4 攻击场景2下的检测函数曲线

Fig. 4 The detection function curve under attack scenario 2

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图 5 攻击场景3下的检测函数曲线

Fig. 5 The detection function curve under attack scenario 3

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图 6 本文所提的方法与不同方法的性能损失函数曲线对比图

Fig. 6 Comparison between the performance index of schemes in this paper and other papers

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表 1 噪声方差及性能指标比较

Table 1 Comparison of noise variance and performance index of different schemes

	$R_{\rm{ADR}} $ (%)	${ {{\boldsymbol{\Sigma}} }_{{u} } }$	$J$
本文方法	75	0.4353	23.2778
本文方法	80	0.5450	29.1433
文献[17]方法	75	0.7615	48.9837
文献[17]方法	80	0.9964	64.0928
文献[18]方法	71.02	1.0010	64.3244
文献[18]方法	81.20	1.5000	96.4850
文献[15]方法	75	2.7166	174.7383
文献[15]方法	80	3.4018	218.8113

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