Passive Attack Detection for a Class of Stealthy Intermittent Integrity Attacks

Kangkang Zhang; Christodoulos Keliris; Thomas Parisini; Bin Jiang; Marios M. Polycarpou

doi:10.1109/JAS.2023.123177

Volume 10 Issue 4

Apr. 2023

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

CiteScore: 23.5, Top 2% (Q1)
Google Scholar h5-index: 77， TOP 5

Turn off MathJax

Article Contents

Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2023 > 10(4): 898-915

K. K. Zhang, C. Keliris, T. Parisini, B. Jiang, and M. M. Polycarpou, “Passive attack detection for a class of stealthy intermittent integrity attacks,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 4, pp. 898–915, Apr. 2023. doi: 10.1109/JAS.2023.123177

Citation:

K. K. Zhang, C. Keliris, T. Parisini, B. Jiang, and M. M. Polycarpou, “Passive attack detection for a class of stealthy intermittent integrity attacks,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 4, pp. 898–915, Apr. 2023. doi: 10.1109/JAS.2023.123177

Citation:

PDF( 2063 KB)

Passive Attack Detection for a Class of Stealthy Intermittent Integrity Attacks

doi: 10.1109/JAS.2023.123177

1.
Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK, and also with the KIOS Research and Innovation Center of Excellence, University of Cyprus, Nicosia 1678, Cyprus
2.
KIOS Research and Innovation Center of Excellence and the Department of Electrical and Computer Engineering, University of Cyprus, Nicosia 1678, Cyprus
3.
Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK, and with the Department of Engineering and Architecture, University of Trieste, Trieste 34127, Italy, and also with the KIOS Research and Innovation Center of Excellence, University of Cyprus, Nicosia 1678, Cyprus
4.
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

Funds: This work was supported by the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skodowska-Curie (101027980 (CSP-CPS-A-ICA), 739551 (KIOS CoE-TEAMING)), the Italian Ministry for Research in the Framework of the 2017 Program for Research Projects of National Interest (PRIN) (2017YKXYXJ), the National Natural Science Foundation of China (61903188, 62073165, 62020106003), the Natural Science Foundation of Jiangsu Province (BK20190403), the 111 Project (B20007), and the Priority Academic Program Development of Jiangsu Higher Education Institutions

More Information

Author Bio:
Kangkang Zhang received the B.Eng., M.Sc. and Ph.D. degrees in control science and engineering from Henan University of Technology in 2012, Northeastern University in 2014, Nanjing University of Aeronautics and Astronautics (NUAA) in 2018, respectively. During 2017−2018, he was a visiting Ph.D. candidate at University of Kent, UK. From 2019 to 2022, he was a Research Associate at the KIOS Research and Innovation Center, University of Cyprus, Cyprus. He currently works as a Marie SkBodowska-Curie Individual Fellow at the Control and Power Group, Imperial College London, UK. His research interests include fault diagnosis, security of cyber-physical systems and fault-tolerant control. Dr. Zhang is a reviewer for various conferences and journals. His thesis received the CAA Excellent Doctoral Dissertation Award from Chinese Association of Automation

Christodoulos Keliris received the Diploma (Hons.) degree in electrical and computer engineering from the Aristotle University of Thessaloniki, Greece, in 2007, the M.Sc. (Hons.) degree in finance from Imperial College London, UK in 2008, and the Ph.D. degree in electrical engineering from the University of Cyprus, Cyprus, in 2015. He was a Researcher in various European research and operational programmes. His current research interests include fault diagnosis for nonlinear systems, nonlinear control theory, adaptive learning, intelligent systems, and security of cyber-physical systems.Dr. Keliris is a Reviewer for various conferences and journals. His dissertation Pricing Barrier Options received the Best M.Sc. Finance Dissertation Prize

Thomas Parisini (Fellow, IEEE) received the Ph.D. degree in electronic engineering and computer science in 1993 from the University of Genoa, Italy. He was with Politecnico di Milano and since 2010. He holds the Chair of Industrial Control and is Director of Research at Imperial College London, UK. He is a Deputy Director of the KIOS Research and Innovation Centre of Excellence, University of Cyprus, Cyprus. Since 2001 he is also Danieli Endowed Chair of Automation Engineering with University of Trieste, Italy. In 2009−2012 he was Deputy Rector of University of Trieste, Italy. In 2018 he received an Honorary Doctorate from University of Aalborg, Denmark. In 2021, he served as Deputy Chair of the Employment & Education Task Force of the B20, Italy. He authored or coauthored more than 350 research papers in archival journals, book chapters, and international conference proceedings. He is a co-recipient of the IFAC Best Application Paper Prize of the Journal of Process Control, Elsevier, for the three year period 2011−2013 and of the 2004 Outstanding Paper Award of the IEEE Trans. on Neural Networks. He is also a recipient of the 2007 IEEE Distinguished Member Award. In 2016, he was awarded as Principal Investigator at Imperial of the H2020 European Union flagship Teaming Project KIOS Research and Innovation Centre of Excellence led by University of Cyprus, Cyprus. Thomas Parisini serves as 2021−2022 President of the IEEE Control Systems Society and has served as Vice-President for Publications Activities. During 2009−2016 he was the Editor-in-Chief of the IEEE Transactions on Control Systems Technology. Since 2017, he is Editor for Control Applications of Automatica and since 2018 he is the Editor in Chief of the European Journal of Control. Among other activities, he was the Program Chair of the 2008 IEEE Conference on Decision and Control and General Co-Chair of the 2013 IEEE Conference on Decision and Control. Prof. Parisini is a Fellow of the IEEE and of the IFAC

Bin Jiang (Fellow, IEEE) received the Ph.D. degree in automatic control from Northeastern University in 1995. He had ever been a Post-Doctoral Fellow, a Research Fellow, an Invited Professor, and a Visiting Professor in Singapore, France, USA, and Canada, respectively.He is currently a Chair Professor of the Cheung Kong Scholar Program with the Ministry of Education and the Vice President of the Nanjing University of Aeronautics and Astronautics. He has authored eight books and over 100 referred international journal articles. His current research interests include intelligent fault diagnosis and fault tolerant control and their applications to helicopters, satellites, and high-speed trains. He is a fellow of the Chinese Association of Automation (CAA). He was a recipient of the Second-Class Prize of National Natural Science Award of China. He currently serves as an Editor for International Journal of Control, Automation and Systems, an Associate Editor or an Editorial Board Member for a number of journals, such as the IEEE Transactions on Cybernetics, and IEEE Transactions on Neural Networks and Learning Systems. He is also a Chair of Control Systems Chapter in IEEE Nanjing Section, and a member of the IFAC Technical Committee on Fault Detection, Supervision, and Safety of Technical Processes

Marios M. Polycarpou (Fellow, IEEE) is a Professor of electrical and computer engineering and the Director of the KIOS Research and Innovation Center of Excellence at the University of Cyprus, Cyprus. He is also a Member of the Cyprus Academy of Sciences, Letters, and Arts, and an Honorary Professor of Imperial College London. He received the B.A degree in computer science and the B.Sc. in electrical engineering, both from Rice University, USA in 1987, and the M.S. and Ph.D. degrees in electrical engineering from the University of Southern California, USA, in 1989 and 1992, respectively. His teaching and research interests include intelligent systems and networks, adaptive and learning control systems, fault diagnosis, machine learning, and critical infrastructure systems. Dr. Polycarpou has published more than 400 articles in refereed journals, edited books and refereed conference proceedings, and co-authored 7 books. He is also the holder of 6 patents.Prof. Polycarpou received the 2016 IEEE Neural Networks Pioneer Award. He is a Fellow of IEEE and IFAC and the recipient of the 2014 Best Paper Award for the journal Building and Environment (Elsevier). He served as the President of the IEEE Computational Intelligence Society (2012–2013), as the President of the European Control Association (2017–2019), and as the Editor-in-Chief of the IEEE Transactions on Neural Networks and Learning Systems (2004–2010). Prof. Polycarpou serves on the Editorial Boards of the Proceedings of the IEEE, the Annual Reviews in Control, and the Foundations and Trends in Systems and Control. His research work has been funded by several agencies and industry in Europe and the United States, including the prestigious European Research Council (ERC) Advanced Grant, the ERC Synergy Grant and the EU Teaming project. Prof. Polycarpou is the recipient of the 2023 IEEE Frank Rosenblatt Technical Field Award
Corresponding author: Kangkang Zhang, e-mail: kzhang2@ic.ac.uk;kzhang05@ucy.ac.cy
¹ Covariance matrix and mean value are concepts used in Kalman filtering. Since LQ optimal filters have similar form with the Kalman filter, we also use the terminologies “covariance matrix” and “mean value” for the LQ optimal filters.² Regarding adjoint system of a linear system, the definition can be found in [44].
² Regarding adjoint system of a linear system, the definition can be found in [44].
Received Date: 2022-10-27
Accepted Date: 2022-11-11

Available Online: 2023-02-01

Abstract

Abstract

This paper proposes a passive methodology for detecting a class of stealthy intermittent integrity attacks in cyber-physical systems subject to process disturbances and measurement noise. A stealthy intermittent integrity attack strategy is first proposed by modifying a zero-dynamics attack model. The stealthiness of the generated attacks is rigorously investigated under the condition that the adversary does not know precisely the system state values. In order to help detect such attacks, a backward-in-time detection residual is proposed based on an equivalent quantity of the system state change, due to the attack, at a time prior to the attack occurrence time. A key characteristic of this residual is that its magnitude increases every time a new attack occurs. To estimate this unknown residual, an optimal fixed-point smoother is proposed by minimizing a piece-wise linear quadratic cost function with a set of specifically designed weighting matrices. The smoother design guarantees robustness with respect to process disturbances and measurement noise, and is also able to maintain sensitivity as time progresses to intermittent integrity attack by resetting the covariance matrix based on the weighting matrices. The adaptive threshold is designed based on the estimated backward-in-time residual, and the attack detectability analysis is rigorously investigated to characterize quantitatively the class of attacks that can be detected by the proposed methodology. Finally, a simulation example is used to demonstrate the effectiveness of the developed methodology.
- Backward-in-time equivalent quantity,
- fixed-point smoother,
- intermittent integrity attacks

FullText(HTML)

¹ Covariance matrix and mean value are concepts used in Kalman filtering. Since LQ optimal filters have similar form with the Kalman filter, we also use the terminologies “covariance matrix” and “mean value” for the LQ optimal filters.² Regarding adjoint system of a linear system, the definition can be found in [44].
² Regarding adjoint system of a linear system, the definition can be found in [44].

References(56)

References

[1]	A. A. Cardenas, S. Amin, and S. Sastry, “Secure control: Towards survivable cyber-physical systems,” in Proc. 28th Int. Conf. Distributed Computing Systems Workshops, Beijing, China, 2008, pp. 495–500.
[2]	S. M. Dibaji, M. Pirani, D. B. Flamholz, A. M. Annaswamy, K. H. Johansson, and A. Chakrabortty, “A systems and control perspective of CPS security,” Annu. Rev. Control, vol. 47, pp. 394–411, Jan. 2019. doi: 10.1016/j.arcontrol.2019.04.011
[3]	V. L. Do, L. Fillatre, I. Nikiforov, and P. Willett, “Security of SCADA systems against cyber-physical attacks,” IEEE Aerosp. Electron. Syst. Mag., vol. 32, no. 5, pp. 28–45, May 2017. doi: 10.1109/MAES.2017.160047
[4]	A. Hobbs, “The colonial pipeline hack: Exposing vulnerabilities in U.S. cybersecurity,” 2021. [Online]. Available: https://sk.sagepub.com/cases/colonial-pipeline-hack-exposing-vulnerabilities-us-cybersecurity.
[5]	W. L. Duo, M. C. Zhou, and A. Abusorrah, “A survey of cyber attacks on cyber physical systems: Recent advances and challenges,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 5, pp. 784–800, May 2022. doi: 10.1109/JAS.2022.105548
[6]	Y. L. Mo and B. Sinopoli, “Secure control against replay attacks,” in Proc. 47th Annu. Allerton Conf. Communication, Control, and Computing, Monticello, USA, 2009, pp. 911–918.
[7]	R. S. Smith, “Covert misappropriation of networked control systems: Presenting a feedback structure,” IEEE Control Syst. Mag., vol. 35, no. 1, pp. 82–92, Feb. 2015. doi: 10.1109/MCS.2014.2364723
[8]	A. Barboni, H. Rezaee, F. Boem, and T. Parisini, “Detection of covert cyber-attacks in interconnected systems: A distributed model-based approach,” IEEE Trans. Autom. Control, vol. 65, no. 9, pp. 3728–3741, Sept. 2020. doi: 10.1109/TAC.2020.2998765
[9]	A. Teixeira, I. Shames, H. Sandberg, and K. H. Johansson, “A secure control framework for resource-limited adversaries,” Automatica, vol. 51, pp. 135–148, Jan. 2015. doi: 10.1016/j.automatica.2014.10.067
[10]	Q. R. Zhang, K. Liu, D. Y. Han, G. Z. Su, and Y. Q. Xia, “Design of stealthy deception attacks with partial system knowledge,” IEEE Trans. Autom. Control, vol. 68, no. 2, pp. 1069–1076, Feb. 2023. doi: 10.1109/TAC.2022.3146079
[11]	A. Teixeira, K. C. Sou, H. Sandberg, and K. H. Johansson, “Secure control systems: A quantitative risk management approach,” IEEE Control Syst. Mag., vol. 35, no. 1, pp. 24–45, Feb. 2015. doi: 10.1109/MCS.2014.2364709
[12]	H. S. Sánchez, D. Rotondo, T. Escobet, V. Puig, and J. Quevedo, “Bibliographical review on cyber attacks from a control oriented perspective,” Annu. Rev. Control, vol. 48, pp. 103–128, Sept. 2019. doi: 10.1016/j.arcontrol.2019.08.002
[13]	T. Y. Zhang and D. Ye, “False data injection attacks with complete stealthiness in cyber-physical systems: A self-generated approach,” Automatica, vol. 120, p. 109117, Oct. 2020. doi: 10.1016/j.automatica.2020.109117
[14]	K. K. Zhang, C. Keliris, T. Parisini, and M. M. Polycarpou, “Stealthy integrity attacks for a class of nonlinear cyber-physical systems,” IEEE Trans. Autom. Control, vol. 67, no. 12, pp. 6723–6730, Dec. 2022. doi: 10.1109/TAC.2021.3131656
[15]	A. Y. Lu and G. H. Yang, “Input-to-state stabilizing control for cyber-physical systems with multiple transmission channels under denial of service,” IEEE Trans. Autom. Control, vol. 63, no. 6, pp. 1813–1820, 2018. doi: 10.1109/TAC.2017.2751999
[16]	H. Zhang, P. Cheng, L. Shi, and J. M. Chen, “Optimal denial-of-service attack scheduling with energy constraint,” IEEE Trans. Autom. Control, vol. 60, no. 11, pp. 3023–3028, Nov. 2015. doi: 10.1109/TAC.2015.2409905
[17]	H. Zhang, Y. F. Qi, J. F. Wu, L. K. Fu, and L. D. He, “DoS attack energy management against remote state estimation,” IEEE Trans. Control Netw. Syst., vol. 5, no. 1, pp. 383–394, Mar. 2018. doi: 10.1109/TCNS.2016.2614099
[18]	S. Amin, A. A. Cárdenas, and S. S. Sastry, “Safe and secure networked control systems under denial-of-service attacks,” in Proc. 12th Int. Workshop on Hybrid Systems: Computation and Control, San Francisco, USA, 2009, pp. 31–45.
[19]	A. Teixeira, I. Shames, H. Sandberg, and K. H. Johansson, “Revealing stealthy attacks in control systems,” in Proc. 50th Annu. Allerton Conf. Communication, Control, and Computing, Monticello, USA, 2012, pp. 1806–1813.
[20]	F. Pasqualetti, F. Dörfler, and F. Bullo, “Attack detection and identification in cyber-physical systems,” IEEE Trans. Autom. Control, vol. 58, no. 11, pp. 2715–2729, Nov. 2013. doi: 10.1109/TAC.2013.2266831
[21]	R. S. Smith, “A decoupled feedback structure for covertly appropriating networked control systems,” IFAC Proc. Vol., vol. 44, no. 1, pp. 90–95, Jan. 2011. doi: 10.3182/20110828-6-IT-1002.01721
[22]	Y. B. Mao, H. Jafarnejadsani, P. Zhao, E. Akyol, and N. Hovakimyan, “Novel stealthy attack and defense strategies for networked control systems,” IEEE Trans. Autom. Control, vol. 65, no. 9, pp. 3847–3862, Sept. 2020. doi: 10.1109/TAC.2020.2997363
[23]	Y. L. Mo, R. Chabukswar, and B. Sinopoli, “Detecting integrity attacks on SCADA systems,” IEEE Trans. Control Syst. Technol., vol. 22, no. 4, pp. 1396–1407, Jul. 2014. doi: 10.1109/TCST.2013.2280899
[24]	R. M. G. Ferrari and A. M. H. Teixeira, “A switching multiplicative watermarking scheme for detection of stealthy cyber-attacks,” IEEE Trans. Autom. Control, vol. 66, no. 6, pp. 2558–2573, Jun. 2021. doi: 10.1109/TAC.2020.3013850
[25]	A. Hoehn and P. Zhang, “Detection of covert attacks and zero dynamics attacks in cyber-physical systems,” in Proc. American Control Conf., Boston, USA, 2016, pp. 302–307.
[26]	S. Weerakkody and B. Sinopoli, “Detecting integrity attacks on control systems using a moving target approach,” in Proc. 54th IEEE Conf. Decision and Control, Osaka, Japan, 2015, pp. 5820–5826.
[27]	P. Griffioen, S. Weerakkody, and B. Sinopoli, “A moving target defense for securing cyber-physical systems,” IEEE Trans. Autom. Control, vol. 66, no. 5, pp. 2016–2031, May 2021. doi: 10.1109/TAC.2020.3005686
[28]	M. M. Polycarpou and A. J. Helmicki, “Automated fault detection and accommodation: A learning systems approach,” IEEE Trans. Syst. Man Cybern., vol. 25, no. 11, pp. 1447–1458, Nov. 1995. doi: 10.1109/21.467710
[29]	S. X. Ding, Model-Based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools. 2nd ed. London, UK: Springer, 2013.
[30]	M. Blanke, M. Kinnaert, J. Lunze, and M. Staroswiecki, Diagnosis and Fault-Tolerant Control. 2nd ed. Berlin, Germany: Springer, 2006.
[31]	Y. K. Wu, B. Jiang, and N. Y. Lu, “A descriptor system approach for estimation of incipient faults with application to high-speed railway traction devices,” IEEE Trans. Syst. Man Cybern. Syst., vol. 49, no. 10, pp. 2108–2118, Oct. 2019. doi: 10.1109/TSMC.2017.2757264
[32]	K. K. Zhang, B. Jiang, X. G. Yan, and Z. H. Mao, “Incipient fault detection for traction motors of high-speed railways using an interval sliding mode observer,” IEEE Trans. Intell. Transport. Syst., vol. 20, no. 7, pp. 2703–2714, Jul. 2019. doi: 10.1109/TITS.2018.2878909
[33]	C. Keliris, M. M. Polycarpou, and T. Parisini, “An integrated learning and filtering approach for fault diagnosis of a class of nonlinear dynamical systems,” IEEE Trans. Neural Netw. Learning Syst., vol. 28, no. 4, pp. 988–1004, Apr. 2017. doi: 10.1109/TNNLS.2015.2504418
[34]	M. Taheri, K. Khorasani, I. Shames, and N. Meskin, Cyber Attack and machine induced fault detection and isolation methodologies for cyber-physical systems, 2020. [Online]. Available: https://arxiv.org/abs/2009.06196.
[35]	K. K. Zhang, M. M. Polycarpou, and T. Parisini, “Enhanced anomaly detector for nonlinear cyber-physical systems against stealthy integrity attacks,” IFAC-PapersOnLine, vol. 53, no. 2, pp. 13682–13687, Jan. 2020. doi: 10.1016/j.ifacol.2020.12.870
[36]	K. K. Zhang, C. Keliris, M. M. Polycarpou, and T. Parisini, “Detecting stealthy integrity attacks in a class of nonlinear cyber-physical systems: A backward-in-time approach,” Automatica, vol. 141, p. 110262, Jul. 2022. doi: 10.1016/j.automatica.2022.110262
[37]	E. Kontouras, A. Tzes, and L. Dritsas, “Hybrid detection of intermittent cyber-attacks in networked power systems,” Energies, vol. 12, no. 24, p. 4625, Dec. 2019. doi: 10.3390/en12244625
[38]	S. Gao, H. Zhang, Z. P. Wang, C. Huang, and H. C. Yan, “Optimal injection attack strategy for cyber-physical systems under resource constraint: A game approach,” IEEE Trans. Control Netw. Syst., to be published.
[39]	J. Chen and R. J. Patton, Robust Model-Based Fault Diagnosis for Dynamic Systems. Springer, 2012.
[40]	X. D. Zhang, M. M. Polycarpou, and T. Parisini, “Fault diagnosis of a class of nonlinear uncertain systems with Lipschitz nonlinearities using adaptive estimation,” Automatica, vol. 46, no. 2, pp. 290–299, 2010. doi: 10.1016/j.automatica.2009.11.014
[41]	K. K. Zhang, B. Jiang, X. G. Yan, and J. Shen, “Interval sliding mode observer based incipient sensor fault detection with application to a traction device in China railway high-speed,” IEEE Trans. Veh. Technol., vol. 68, no. 3, pp. 2585–2597, 2019. doi: 10.1109/TVT.2019.2894670
[42]	M. Basseville and I. V. Nikiforov, Detection of Abrupt Changes: Theory and Application. Englewood Cliffs: Prentice-Hall, 1993.
[43]	B. D. Anderson and J. B. Moore, Optimal Filtering. North Chelmsford, USA: Courier Corporation, 2012.
[44]	M. Green and D. J. N. Limebeer, Linear Robust Control. New York, USA: Dover Publications, 2012.
[45]	D. Simon, Optimal State Estimation: Kalman, H_∞, and Nonlinear Approaches. Hoboken, USA: John Wiley & Sons, 2006.
[46]	G. A. Einicke, Smoothing, Filtering and Prediction: Estimating the Past, Present and Future. Rijeka: IntechOpen, 2012.
[47]	X. B. Li and K. M. Zhou, “A time domain approach to robust fault detection of linear time-varying systems,” Automatica, vol. 45, no. 1, pp. 94–102, Jan. 2009. doi: 10.1016/j.automatica.2008.07.017
[48]	R. N. Banavar and J. L. Speyer, “A linear-quadratic game approach to estimation and smoothing,” in Proc. American Control Conf., Boston, USA, 1991, pp. 2818–2822.
[49]	H. Abou-Kandil, G. Freiling, V. Ionescu, and G. Jank, Matrix Riccati Equations in Control and Systems Theory. Birkhäuser Verlag, Basel, 2012.
[50]	G. Basile and G. Marro, Controlled and Conditioned Invariants in Linear System Theory. Englewood Cliffs: Prentice Hall, 1992.
[51]	Q. J. Xia, M. Rao, Y. Q. Ying, and X. M. Shen, “Adaptive fading Kalman filter with an application,” Automatica, vol. 30, no. 8, pp. 1333–1338, Aug. 1994. doi: 10.1016/0005-1098(94)90112-0
[52]	Y. J. Zhang, J. F. Zhang, X. K. Liu, and Z. Liu, “Quantized-output feedback model reference control of discrete-time linear systems,” Automatica, vol. 137, p. 110027, Mar. 2022. doi: 10.1016/j.automatica.2021.110027
[53]	J. Guo, Y. J. Zhang, J. F. Zhang, and X. K. Liu, “Finite quantized-output feedback tracking control of possibly non-minimum phase linear systems,” IEEE Control Syst. Lett., vol. 6, pp. 2407–2412, Mar. 2022. doi: 10.1109/LCSYS.2022.3159130
[54]	M. L. Lv, W. W. Yu, J. D. Cao, and S. Baldi, “A separation-based methodology to consensus tracking of switched high-order nonlinear multiagent systems,” IEEE Trans. Neural Netw. Learn. Syst., vol. 33, no. 10, pp. 5467–5479, Oct. 2022. doi: 10.1109/TNNLS.2021.3070824
[55]	M. L. Lv, B. De Schutter, C. Shi, and S. Baldi, “Logic-based distributed switching control for agents in power-chained form with multiple unknown control directions,” Automatica, vol. 137, p. 110143, Mar. 2022. doi: 10.1016/j.automatica.2021.110143
[56]	Y. Liu, D. Y. Yao, L. J. Wang, and S. J. Lu, “Distributed adaptive fixed-time robust platoon control for fully heterogeneous vehicles,” IEEE Trans. Syst. Man Cybern. Syst., vol. 53, no. 1, pp. 264–274, Jan. 2023. doi: 10.1109/TSMC.2022.3179444

Supplements(0)

Cited By

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Figures(8)

Get Citation

PDF

XML

Article Metrics

Article views (552) PDF downloads(106)

Highlights

A stealthy intermittent integrity attack generation strategy is formulated, which does not require that the adversary has precise knowledge of the system states. A backward-in-time detection residual is formulated, which increases in magnitude each time a new attack occurs
An optimal fixed-point smoother with covariance matrix resetting is proposed to implement the aforementioned backward-in-time residual. Such a smoother guarantees robustness to both disturbances and noise, and can also reset the covariance matrix to maintain sensitivity to intermittent integrity attacks
The corresponding adaptive threshold is designed, and an attack detectability analysis is carried out to characterize quantitatively the class of detectable stealthy intermittent integrity attacks

Passive Attack Detection for a Class of Stealthy Intermittent Integrity Attacks

doi: 10.1109/JAS.2023.123177

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Highlights

Export File

Citation

Format

Content