Abstract
Resilient observer design for Cyber-Physical Systems (CPS) in the presence of adversarial false data injection attacks (FDIA) is an active area of research. The existing state-of-the-art algorithms tend to break down as more and more knowledge of the system is built into the attack model; also as the percentage of attacked nodes increases. From the view of optimization theory, the problem is often cast as a classical error correction problem for which a theoretical limit of \(50\%\) has been established as the maximum percentage attacked nodes for which state recovery is guaranteed. Beyond this limit, the performance of \(\ell _1\)-minimization based schemes, for instance, deteriorates rapidly. Similar performance degradation occurs for other types of resilient observers beyond certain percentages of attacked nodes. In order to increase the corresponding percentage of attacked nodes for which state recoveries can be guaranteed, researchers have begun to incorporate prior information into the underlying resilient observer design framework. For the most pragmatic cases, this prior information is often obtained through a data-driven machine learning process. Existing results have shown a strong positive correlation between the maximum attacked percentages that can be tolerated and the accuracy of the data-driven model. Motivated by these results, this chapter examines the case for pruning algorithms designed to improve the Positive Prediction Value (PPV) of the resulting prior information, given stochastic uncertainty characteristics of the underlying machine learning model. Theoretical quantification of the achievable improvement is given. Simulation results show that the pruning algorithm significantly increases the maximum correctable percentage of attacked nodes, even for machine learning model whose prediction power is comparable to the random flip of a coin.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
0: safe, 1: unsafe.
References
M. Abbaszadeh, L.K. Mestha, C. Bushey, D.F. Holzhauer, Automated attack localization and detection. U.S. Patent No. 10,417,415 (2019)
O.M. Anubi, C. Konstantinou, R. Roberts, Resilient optimal estimation using measurement prior (2019). arXiv: 1907.13102
O.M. Anubi, C. Konstantinou, C.A. Wong, S. Vedula, Multi-model resilient observer under false data injection attacks, in 2020 IEEE Conference on Control Technology and Applications (CCTA) (IEEE, 2020), pp. 1–8
O.M. Anubi, L. Mestha, H. Achanta, Robust resilient signal reconstruction under adversarial attacks (2018). arXiv:1807.08004
O.M. Anubi, C. Konstantinou, Enhanced resilient state estimation using data-driven auxiliary models. IEEE Trans. Ind. Inf. 16(1), 639–647 (2019)
M. Bishop, What is computer security? IEEE Sec. Priv. 1(1), 67–69 (2003)
A. Burg, A. Chattopadhyay, K.Y. Lam, Wireless communication and security issues for cyber-physical systems and the Internet-of-Things. Proc. IEEE 106(1), 38–60 (2017)
E.J. Candes, T. Tao, Decoding by linear programming. IEEE Trans. Inf. Theory 51(12), 4203–4215 (2005)
E. Candes, T. Tao, The Dantzig selector: statistical estimation when p is much larger than n. Ann. Stat. 35(6), 2313–2351 (2007)
E.J. Candès, J. Romberg, T. Tao, Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theory 52(2), 489–509 (2006)
T.M. Chen, S. Abu-Nimeh, Lessons from stuxnet. Computer 44(4), 91–93 (2011)
M.S. Chong, M. Wakaiki, J.P. Hespanha, Observability of linear systems under adversarial attacks, in 2015 American Control Conference (ACC). (IEEE, 2015), pp. 2439–2444
A.O. de Sá, L.F.R. da Costa Carmo, R.C. Machado, Covert attacks in cyber-physical control systems. IEEE Trans. Ind. Inf. 13(4), 1641–1651 (2017)
Y. Deldjoo, T.D. Noia, F.A. Merra, A survey on adversarial recommender systems: from attack/defense strategies to generative adversarial networks. ACM Comput. Surv. (CSUR) 54(2), 1–38 (2021)
P. Derler, E.A. Lee, A.S. Vincentelli, Modeling cyber-physical systems. Proc. IEEE 100(1), 13–28 (2011)
R. Dhaouadi, A.A. Hatab, Dynamic modelling of differential-drive mobile robots using lagrange and newton-euler methodologies: A unified framework. Advances in Robotics & Automation 2(2), 1–7 (2013)
D.L. Donoho, Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)
M. Esmalifalak, L. Liu, N. Nguyen, R. Zheng, Z. Han, Detecting stealthy false data injection using machine learning in smart grid. IEEE Syst. J. 11(3), 1644–1652 (2014)
C. Fang, Y. Qi, P. Cheng, W.X. Zheng, Optimal periodic watermarking schedule for replay attack detection in cyber-physical systems. Automatica 112, 108698 (2020)
T. Fawcett, An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861–874 (2006)
H. Fawzi, P. Tabuada, S. Diggavi, Secure estimation and control for cyber-physical systems under adversarial attacks. IEEE Trans. Autom. Control 59(6), 1454–1467 (2014)
Manuel Fernández, Stuart Williams, Closed-form expression for the poisson-binomial probability density function. IEEE Trans. Aerosp. Electron. Syst. 46(2), 803–817 (2010)
M. Fornasier, H. Rauhut, Compressive Sensing. Handbook of Math. Methods Imaging 1, 187–229 (2015)
M.P. Friedlander, H. Mansour, R. Saab, Ö. Yilmaz, Recovering compressively sampled signals using partial support information. IEEE Trans. Inf. Theory 58(2), 1122–1134 (2011)
Z. Guo, D. Shi, K.H. Johansson, L. Shi, Optimal linear cyber-attack on remote state estimation. IEEE Trans. Control Netw. Syst. 4(1), 4–13 (2016)
Y. He, G.J. Mendis, J. Wei, Real-time detection of false data injection attacks in smart grid: a deep learning-based intelligent mechanism. IEEE Trans. Smart Grid 8(5), 2505–2516 (2017)
Y. Huang, J. Tang, Y. Cheng, H. Li, K.A. Campbell, Z. Han, Real-time detection of false data injection in smart grid networks: an adaptive CUSUM method and analysis. IEEE Syst. J. 10(2), 532–543 (2014)
S.K. Khaitan, J.D. McCalley, Design techniques and applications of cyber physical systems: a survey. IEEE Syst. J. 9(2), 350–365 (2014)
E.A. Lee, CPS foundations, in Design Automation Conference (IEEE, 2010), pp. 737–742
C. Lee, H. Shim, Y. Eun, Secure and robust state estimation under sensor attacks, measurement noises, and process disturbances: Observer-based combinatorial approach, in 2015 European Control Conference (ECC), (IEEE, 2015), pp. 1872–1877
R.M. Lee, M.J. Assante, T. Conway, German steel mill cyber attack. Ind. Control Syst. 30, 62 (2014)
Y. Liu, P. Ning, M.K. Reiter, False data injection attacks against state estimation in electric power grids. ACM Trans. Inf. Syst. Sec. (TISSEC) 14(1), 1–33 (2011)
M. Liu, G. Chowdhary, B.C. Da Silva, S.Y. Liu, J.P. How, Gaussian processes for learning and control: a tutorial with examples. IEEE Control Syst. Mag. 38(5), 53–86 (2018)
H. Liu, Y. Mo, J. Yan, L. Xie, K.H. Johansson, An online approach to physical watermark design. IEEE Trans. Autom. Control 65(9), 3895–3902 (2020)
Y. Mo, B. Sinopoli, False data injection attacks in control systems, in Preprints of the 1st workshop on Secure Control Systems (2010), pp. 1–6
Y. Mo, B. Sinopoli, On the performance degradation of cyber-physical systems under stealthy integrity attacks. IEEE Trans. Autom. Control 61(9), 2618–2624 (2015)
Y. Nakahira, Y. Mo, Attack-Resilient \(\mathscr{H}_2, \mathscr{H}_{\infty }, \,\text{and} \,\ell _1\) state estimator. IEEE Trans. Autom. Control 63(12), 4353–4360 (2018)
New York control area load zone map. [Online]. https://www.nyiso.com/documents/20142/1397960/nyca_zonemaps.pdf
N. Y. I. S. Operator, "Load Data," [Online]. https://www.nyiso.com/load-data
M. Ozay, I. Esnaola, F.T.Y. Vural, S.R. Kulkarni, H.V. Poor, Machine learning methods for attack detection in the smart grid. IEEE Trans. Neural Netw. Learn. Syst. 27(8), 1773–1786 (2015)
M., Pajic, P. Tabuada, I. Lee, G.J. Pappas, Attack-resilient state estimation in the presence of noise, in 2015 54th IEEE Conference on Decision and Control (CDC) (IEEE, 2015), pp. 5827–5832
F. Pasqualetti, F. Dörfler, F. Bullo, Attack detection and identification in cyber-physical systems. IEEE Trans. Autom. Control 58(11), 2715–2729 (2013)
K. Pelechrinis, M. Iliofotou, S.V. Krishnamurthy, Denial of service attacks in wireless networks: the case of jammers. IEEE Commun. Surv. Tutor. 13(2), 245–257 (2010)
Power System Test Case Archive, 14 bus power flow test case. [Online]. http://labs.ece.uw.edu/pstca/pf14/pg_tca14bus.htm
C.E. Rasmussen, Gaussian processes in machine learning, in Summer school on machine learning (Springer, Berlin, Heidelberg, 2003), pp. 63–71
Scholtz, E. (2004). Observer-based monitors and distributed wave controllers for electromechanical disturbances in power systems (Doctoral dissertation, Massachusetts Institute of Technology)
T. Shinohara, T. Namerikawa, Z. Qu, Resilient reinforcement in secure state estimation against sensor attacks with a priori information. IEEE Trans. Autom. Control 64(12), 5024–5038 (2019)
Y. Shoukry, P. Tabuada, Event-triggered state observers for sparse sensor noise/attacks. IEEE Trans. Autom. Control 61(8), 2079–2091 (2015)
Y. Shoukry, P. Nuzzo, A. Puggelli, A.L. Sangiovanni-Vincentelli, S.A. Seshia, P. Tabuada, Secure state estimation for cyber-physical systems under sensor attacks: a satisfiability modulo theory approach. IEEE Trans. Autom. Control 62(10), 4917–4932 (2017)
J. Slay, M. Miller, Lessons learned from the maroochy water breach, in International Conference on Critical Infrastructure Protection. (Springer, Boston, MA, 2007), pp. 73–82
T. Sui, Y. Mo, D. Marelli, X.M. Sun, M. Fu, The Vulnerability of Cyber-Physical System under Stealthy Attacks. IEEE Trans. Autom. Control (2020)
R. Urtasun, T. Darrell, Sparse probabilistic regression for activity-independent human pose inference, in 2008 IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2008), pp. 1–8
S. Weerakkody, B. Sinopoli, Detecting integrity attacks on control systems using a moving target approach, in 2015 54th IEEE Conference on Decision and Control (CDC) (IEEE, 2015), pp. 5820–5826
J. Yang, C. Zhou, Y.C. Tian, C. An, A Zoning-Based Secure Control Approach Against Actuator Attacks in Industrial Cyber-Physical Systems. IEEE Trans. Industr. Electron. 68(3), 2637–2647 (2020)
K. Zetter, A cyber attack has caused confirmed physical damage for the second time ever (2015)
Y. Zheng, O.M. Anubi, Attack-resilient observer pruning for path-tracking control of wheeled mobile robot, in 2020 ASME Dynamic Systems and Control(DSC) Conference, ASME (2020), pp. 1–9
Y. Zheng, O.M. Anubi, Attack-resilient weighted \(\ell _1\) observer with prior pruning, in 2021 American Control Conference (ACC) (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Zheng, Y., Anubi, O.M. (2022). Resilient Observer Design for Cyber-Physical Systems with Data-Driven Measurement Pruning. In: Abbaszadeh, M., Zemouche, A. (eds) Security and Resilience in Cyber-Physical Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-97166-3_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-97166-3_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-97165-6
Online ISBN: 978-3-030-97166-3
eBook Packages: EngineeringEngineering (R0)