Dynamic Data Injection Attack Detection of Cyber Physical Power Systems With Uncertainties

Understanding potential behaviors of attackers is of paramount importance for improving the cybersecurity of power systems. However, the attack behaviors in existing studies are often modeled statically on a single snapshot, which neglects the reality of a dynamically time-evolving power system. Accordingly, a dynamic cyber-attack model with local network information is proposed to characterize the typical data injection attack with the integration of potential dynamic behaviors of an attacker. The proposed model collaboratively alters the meter measurement in a stealthy way to illegally contaminate the system state, thus posing severe threats to cyber physical power systems. We then develop a novel anomaly detection countermeasure from the perspective of state estimation to effectively recognize the dynamic injection attack. In this countermeasure, an interval state forecasting method is proposed to approximate the possible largest variation bounds of each state variable based on a worst-case analysis considering the forecasting uncertainties of renewable energy sources, electric loads, and network parameter perturbations. In addition, the kernel quantile regression is introduced and implemented to formulate the uncertainties in renewable energy and electric load forecast as a series of confidence intervals. When any state variable falls outside its preforecasted intervals, the proposed countermeasure detects the anomaly and sets an alarm condition indicating the possibility of data contamination. Finally, the results from our extensive studies on several IEEE standard test systems have been presented to demonstrate the feasibility of the dynamic attack and the effectiveness of the detection countermeasure.

[1]  Jinping Hao,et al.  Optimal malicious attack construction and robust detection in Smart Grid cyber security analysis , 2014, 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[2]  Zuyi Li,et al.  Modeling of Local False Data Injection Attacks With Reduced Network Information , 2015, IEEE Transactions on Smart Grid.

[3]  Jinping Hao,et al.  Sparse Malicious False Data Injection Attacks and Defense Mechanisms in Smart Grids , 2015, IEEE Transactions on Industrial Informatics.

[4]  Jose Daniel Lara,et al.  Robust Energy Management Systems for Isolated Microgrids Under Uncertainty , 2014 .

[5]  Zuyi Li,et al.  Local Load Redistribution Attacks in Power Systems With Incomplete Network Information , 2014, IEEE Transactions on Smart Grid.

[6]  Aditya Ashok,et al.  Online Detection of Stealthy False Data Injection Attacks in Power System State Estimation , 2018, IEEE Transactions on Smart Grid.

[7]  Lamine Mili,et al.  A Generalized False Data Injection Attacks Against Power System Nonlinear State Estimator and Countermeasures , 2018, IEEE Transactions on Power Systems.

[8]  Haibo He,et al.  Q-Learning-Based Vulnerability Analysis of Smart Grid Against Sequential Topology Attacks , 2017, IEEE Transactions on Information Forensics and Security.

[9]  Kit Po Wong,et al.  Forecasting-Aided Imperfect False Data Injection Attacks Against Power System Nonlinear State Estimation , 2016, IEEE Transactions on Smart Grid.

[10]  L. Mili,et al.  A Robust Iterated Extended Kalman Filter for Power System Dynamic State Estimation , 2017, IEEE Transactions on Power Systems.

[11]  Hui Liu,et al.  A matrix-perturbation-theory-based optimal strategy for small-signal stability analysis of large-scale power grid , 2018 .

[12]  Mehul Motani,et al.  Detecting False Data Injection Attacks in AC State Estimation , 2015, IEEE Transactions on Smart Grid.

[13]  Wei Dong,et al.  Robust and Secure Time-Synchronization Against Sybil Attacks for Sensor Networks , 2015, IEEE Transactions on Industrial Informatics.

[14]  Victor O. K. Li,et al.  Online False Data Injection Attack Detection With Wavelet Transform and Deep Neural Networks , 2018, IEEE Transactions on Industrial Informatics.

[15]  J. Lofberg,et al.  YALMIP : a toolbox for modeling and optimization in MATLAB , 2004, 2004 IEEE International Conference on Robotics and Automation (IEEE Cat. No.04CH37508).

[16]  Zhigang Chu,et al.  False data injection attacks on power system state estimation with limited information , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[17]  Siddharth Sridhar,et al.  Model-Based Attack Detection and Mitigation for Automatic Generation Control , 2014, IEEE Transactions on Smart Grid.

[18]  Lingzhi Zhu,et al.  Novel Detection Scheme Design Considering Cyber Attacks on Load Frequency Control , 2018, IEEE Transactions on Industrial Informatics.

[19]  Yutian Liu,et al.  Calculation of Critical Oscillation Modes for Large Delayed Cyber-Physical Power System Using Pseudo-Spectral Discretization of Solution Operator , 2018, IEEE Transactions on Power Systems.

[20]  Yitao Liu,et al.  Deep learning based ensemble approach for probabilistic wind power forecasting , 2017 .

[21]  Ali Abur,et al.  Highly Efficient Implementation for Parameter Error Identification Method Exploiting Sparsity , 2017, IEEE Transactions on Power Systems.

[22]  Zhiwei Wang,et al.  An Identity-Based Data Aggregation Protocol for the Smart Grid , 2017, IEEE Transactions on Industrial Informatics.

[23]  Oliver Kosut,et al.  Vulnerability Analysis and Consequences of False Data Injection Attack on Power System State Estimation , 2015, IEEE Transactions on Power Systems.

[24]  Minrui Fei,et al.  Resilient Event-Triggering $H_{\infty }$ Load Frequency Control for Multi-Area Power Systems With Energy-Limited DoS Attacks , 2017, IEEE Transactions on Power Systems.

[25]  George Cybenko,et al.  Engineering Statistical Behaviors for Attacking and Defending Covert Channels , 2013, IEEE Journal of Selected Topics in Signal Processing.

[26]  Johan Löfberg,et al.  YALMIP : a toolbox for modeling and optimization in MATLAB , 2004 .

[27]  Husheng Li,et al.  Time Synchronization Attack in Smart Grid: Impact and Analysis , 2013, IEEE Transactions on Smart Grid.

[28]  Yitao Liu,et al.  Deep Learning-Based Interval State Estimation of AC Smart Grids Against Sparse Cyber Attacks , 2018, IEEE Transactions on Industrial Informatics.

[29]  Yanfei Sun,et al.  Strategic Honeypot Game Model for Distributed Denial of Service Attacks in the Smart Grid , 2017, IEEE Transactions on Smart Grid.

[30]  Zhigang Chu,et al.  Can Attackers With Limited Information Exploit Historical Data to Mount Successful False Data Injection Attacks on Power Systems? , 2017, IEEE Transactions on Power Systems.

[31]  Yitao Liu,et al.  Deep belief network based deterministic and probabilistic wind speed forecasting approach , 2016 .

[32]  Zhao Yang Dong,et al.  A Review of False Data Injection Attacks Against Modern Power Systems , 2017, IEEE Transactions on Smart Grid.

[33]  B. K. Panigrahi,et al.  Joint-Transformation-Based Detection of False Data Injection Attacks in Smart Grid , 2018, IEEE Transactions on Industrial Informatics.

[34]  Mohammad Shahidehpour,et al.  Cyber-Attack on Overloading Multiple Lines: A Bilevel Mixed-Integer Linear Programming Model , 2018, IEEE Transactions on Smart Grid.

[35]  Bharadwaj Satchidanandan,et al.  An Online Detection Framework for Cyber Attacks on Automatic Generation Control , 2017, IEEE Transactions on Power Systems.

[36]  H. Vincent Poor,et al.  Strategic Protection Against Data Injection Attacks on Power Grids , 2011, IEEE Transactions on Smart Grid.

[37]  Robert H. Deng,et al.  Resonance Attacks on Load Frequency Control of Smart Grids , 2018, IEEE Transactions on Smart Grid.

[38]  Ming Qiu,et al.  An Interval Power Flow Analysis Through Optimizing-Scenarios Method , 2018, IEEE Transactions on Smart Grid.

[39]  Yongqiao Wang,et al.  Value at risk estimation based on generalized quantile regression , 2009, 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems.

[40]  Alessandro Barenghi,et al.  Fault Injection Attacks on Cryptographic Devices: Theory, Practice, and Countermeasures , 2012, Proceedings of the IEEE.

[41]  C. Rakpenthai,et al.  State Estimation of Power System Considering Network Parameter Uncertainty Based on Parametric Interval Linear Systems , 2012, IEEE Transactions on Power Systems.

[42]  Zuyi Li,et al.  False Data Injection Attacks Induced Sequential Outages in Power Systems , 2019, IEEE Transactions on Power Systems.

[43]  Chongqing Kang,et al.  Modeling Conditional Forecast Error for Wind Power in Generation Scheduling , 2014, IEEE Transactions on Power Systems.

[44]  Jin Wei,et al.  Real-Time Detection of False Data Injection Attacks in Smart Grid: A Deep Learning-Based Intelligent Mechanism , 2017, IEEE Transactions on Smart Grid.