Estimating the Impact of Cyber-Attack Strategies for Stochastic Control Systems.

Risk assessment is an inevitable step in implementation of a cyber-defense strategy. An important part of this assessment is to reason about the impact of possible attacks. In this paper, we propose a framework for estimating the impact of cyber-attacks in stochastic linear control systems. The framework can be used to estimate the impact of denial of service, rerouting, sign alternation, replay, false data injection, and bias injection attacks. For the stealthiness constraint, we adopt the Kullback-Leibler divergence between residual sequences during the attack. Two impact metrics are considered: (1) The probability that some of the critical states leave a safety region; and (2) The expected value of the infinity norm of the critical states. For the first metric, we prove that the impact estimation problem can be reduced to a set of convex optimization problems. Thus, the exact solution can be found efficiently. For the second metric, we derive an efficient to calculate lower bound. Finally, we demonstrate how the framework can be used for risk assessment on an example.

[1]  Carlos Murguia,et al.  Model-based Attack Detection Scheme for Smart Water Distribution Networks , 2017, AsiaCCS.

[2]  Nathan van de Wouw,et al.  Reachable Sets of Hidden CPS Sensor Attacks: Analysis and Synthesis Tools , 2017 .

[3]  Dragan Nesic,et al.  Security Metrics of Networked Control Systems under Sensor Attacks (extended preprint) , 2018, ArXiv.

[4]  Michel Kinnaert,et al.  Diagnosis and Fault-Tolerant Control , 2004, IEEE Transactions on Automatic Control.

[5]  Bruno Sinopoli,et al.  Modeling impact of attacks, recovery, and attackability conditions for situational awareness , 2014, 2014 IEEE International Inter-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA).

[6]  Bruno Sinopoli,et al.  A Graph-Theoretic Characterization of Perfect Attackability for Secure Design of Distributed Control Systems , 2017, IEEE Transactions on Control of Network Systems.

[7]  Soummya Kar,et al.  Optimal Attack Strategies Subject to Detection Constraints Against Cyber-Physical Systems , 2016, IEEE Transactions on Control of Network Systems.

[8]  Ling Shi,et al.  Worst-case stealthy innovation-based linear attack on remote state estimation , 2018, Autom..

[9]  Petros G. Voulgaris,et al.  On the Computation of Worst Attacks: a LP Framework , 2018, 2018 Annual American Control Conference (ACC).

[10]  Karl Henrik Johansson,et al.  Quantifying the Impact of Cyber-Attack Strategies for Control Systems Equipped With an Anomaly Detector , 2018, 2018 European Control Conference (ECC).

[11]  S. Shankar Sastry,et al.  Safe and Secure Networked Control Systems under Denial-of-Service Attacks , 2009, HSCC.

[12]  Peter Palensky,et al.  Combined data integrity and availability attacks on state estimation in cyber-physical power grids , 2016, 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[13]  Anguluri Rajasekhar,et al.  Periodic coordinated attacks against cyber-physical systems: Detectability and performance bounds , 2016, 2016 IEEE 55th Conference on Decision and Control (CDC).

[14]  Karl Henrik Johansson,et al.  Voltage control for interconnected microgrids under adversarial actions , 2015, 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA).

[15]  Henrik Sandberg,et al.  Limiting the Impact of Stealthy Attacks on Industrial Control Systems , 2016, CCS.

[16]  Karl Henrik Johansson,et al.  A secure control framework for resource-limited adversaries , 2012, Autom..

[17]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[18]  Henrik Sandberg,et al.  Security analysis of control system anomaly detectors , 2017, 2017 American Control Conference (ACC).

[19]  Karl Henrik Johansson,et al.  Exploiting Submodularity in Security Measure Allocation for Industrial Control Systems , 2017, SafeThings@SenSys.

[20]  G. Stoneburner,et al.  Risk Management Guide for Information Technology Systems: Recommendations of the National Institute of Standards and Technology , 2002 .

[21]  S. Shankar Sastry,et al.  Research Challenges for the Security of Control Systems , 2008, HotSec.

[22]  Riccardo M. G. Ferrari,et al.  Detection and isolation of routing attacks through sensor watermarking , 2017, 2017 American Control Conference (ACC).

[23]  Ilija Jovanov,et al.  Sporadic data integrity for secure state estimation , 2017, 2017 IEEE 56th Annual Conference on Decision and Control (CDC).

[24]  Carlos Murguia,et al.  Tuning Windowed Chi-Squared Detectors for Sensor Attacks , 2017, 2018 Annual American Control Conference (ACC).

[25]  Karl Henrik Johansson,et al.  Analysis and Mitigation of Bias Injection Attacks Against a Kalman Filter , 2017 .

[26]  B. Brumback,et al.  A Chi-square test for fault-detection in Kalman filters , 1987 .

[27]  Jill Slay,et al.  Lessons Learned from the Maroochy Water Breach , 2007, Critical Infrastructure Protection.

[28]  Alvaro A. Cárdenas,et al.  Attacks against process control systems: risk assessment, detection, and response , 2011, ASIACCS '11.

[29]  D. Kushner,et al.  The real story of stuxnet , 2013, IEEE Spectrum.

[30]  Charles W. Champ,et al.  A multivariate exponentially weighted moving average control chart , 1992 .

[31]  Bruno Sinopoli,et al.  Detecting Integrity Attacks on SCADA Systems , 2014, IEEE Transactions on Control Systems Technology.

[32]  Vijay Gupta,et al.  On Kalman Filtering with Compromised Sensors: Attack Stealthiness and Performance Bounds , 2017, IEEE Transactions on Automatic Control.

[33]  Emanuele Garone,et al.  False data injection attacks against state estimation in wireless sensor networks , 2010, 49th IEEE Conference on Decision and Control (CDC).