White-Box Concealment Attacks Against Anomaly Detectors for Cyber-Physical Systems
Pages 111 - 131
Abstract
Anomaly detection for cyber-physical systems is an effective method to detect ongoing process anomalies caused by an attacker. Recently, a number of anomaly detection techniques were proposed (e.g., ML based, invariant rule based, control theoretical). Little is known about the resilience of those anomaly detectors against attackers that conceal their attacks to evade detection. In particular, their resilience against white-box concealment attacks has so far only been investigated for the subset of neural network-based detectors. In this work, we demonstrate for the first time that white-box concealment attacks can also be applied to detectors that are not based on neural network solutions. In order to achieve this, we propose a generic white-box attack that evades anomaly detectors and can be adapted even if the target detection technique does not optimize a loss function. We design and implement a framework to perform our attacks, and test it on several detectors from related work. Our results show that it is possible to completely evade a wide range of detectors (based on diverse detection techniques) while reducing the number of samples that need to be manipulated (compared to prior black-box concealment attacks).
References
[1]
Adepu, S., Mathur, A.: Distributed detection of single-stage multipoint cyber attacks in a water treatment plant. In: Proceedings of the ACM ASIA Conference on Computer and Communications Security (ASIACCS) (2016)
[2]
Ahmed, C.M., et al.: Noiseprint: attack detection using sensor and process noise fingerprint in cyber physical systems. In: Proceedings of the Asia Conference on Computer and Communications Security (AsiaCCS) (2018)
[3]
Aoudi, W., Iturbe, M., Almgren, M.: Truth will out: departure-based process-level detection of stealthy attacks on control systems. In: Proceedings of the ACM Conference on Computer and Communications Security (CCS). ACM (2018)
[4]
Biggio, B., et al.: Evasion attacks against machine learning at test time. In: Blockeel, H., Kersting, K., Nijssen, S., Železný, F. (eds.) Machine Learning and Knowledge Discovery in Databases, pp. 387–402 (2013)
[5]
Cao, Y., et al.: Adversarial sensor attack on lidar-based perception in autonomous driving. In: Proceedings of the ACM Conference on Computer and Communications Security (CCS), p. 2267–2281. ACM, New York, NY, USA (2019)
[6]
Cárdenas, A., Amin, S., Sinopoli, B., Giani, A., Perrig, A., Sastry, S.S.: Challenges for securing cyber physical systems. In: Workshop on Future Directions in Cyber-physical Systems Security. DHS, July 2009
[7]
Cervini, J., Rubin, A., Watkins, L.: Don’t drink the cyber: extrapolating the possibilities of Oldsmar’s water treatment cyberattack. In: International Conference on Cyber Warfare and Security, vol. 17, pp. 19–25 (2022)
[8]
Chen, Y., Poskitt, C.M., Sun, J.: Learning from mutants: using code mutation to learn and monitor invariants of a cyber-physical system. In: Proceedings of the IEEE Symposium on Security and Privacy, pp. 648–660. IEEE (2018)
[9]
Choi, H., et al.: Detecting attacks against robotic vehicles: a control invariant approach. In: Proceedings of the ACM Conference on Computer and Communications Security (CCS) (2018)
[10]
Dahlmanns, M., Lohmöller, J., Fink, I.B., Pennekamp, J., Wehrle, K., Henze, M.: Easing the conscience with OPC UA: an internet-wide study on insecure deployments. In: Proceedings of the ACM Internet Measurement Conference (2020)
[11]
Erba, A., et al.: Constrained concealment attacks against reconstruction-based anomaly detectors in industrial control systems. In: Proceedings of the Annual Computer Security Applications Conference (ACSAC), December 2020
[12]
Erba, A., Tippenhauer, N.O.: Assessing model-free anomaly detection in industrial control systems against generic concealment attacks. In: Proceedings of the Annual Computer Security Applications Conference (ACSAC). Austin, USA, December 2022
[13]
Feng, C., Palleti, V.R., Mathur, A., Chana, D.: A systematic framework to generate invariants for anomaly detection in industrial control systems. In: Proceedings of Network and Distributed System Security Symposium (NDSS) (2019)
[14]
Galloway B, Hancke GP, et al. Introduction to industrial control networks IEEE Commun. Surv. Tutor. 2013 15 2 860-880
[15]
Garcia, L., Brasser, F., Cintuglu, M.H., Sadeghi, A.R., Mohammed, O., Zonouz, S.A.: Hey, my malware knows physics! attacking PLCs with physical model aware rootkit. In: Proceedings of Network and Distributed System Security Symposium (NDSS), February 2017
[16]
Goh, J., Adepu, S., Tan, M., Lee, Z.S.: Anomaly detection in cyber physical systems using recurrent neural networks. In: 2017 IEEE 18th International Symposium on High Assurance Systems Engineering (HASE), pp. 140–145. IEEE (2017)
[17]
Golub, G.H., Reinsch, C.: Singular value decomposition and least squares solutions. In: Bauer, F.L. (eds.) Linear Algebra, vol. 2, pp. 134–151. Springer, Heidelberg (1971).
[18]
Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. CoRR abs/1412.6572 (2014)
[19]
Hadžiosmanović, D., Sommer, R., Zambon, E., Hartel, P.H.: Through the eye of the plc: semantic security monitoring for industrial processes. In: Proceedings of the Annual Computer Security Applications Conference (ACSAC), pp. 126–135. ACM, New York, NY, USA (2014)
[20]
Hayes, M.H.: Statistical Digital Signal Processing and Modeling. Wiley, Hoboken (2009)
[21]
Koubâa, A., Allouch, A., Alajlan, M., Javed, Y., Belghith, A., Khalgui, M.: Micro air vehicle link (MAVlink) in a nutshell: a survey. IEEE Access 7 (2019)
[22]
Kravchik, M., Shabtai, A.: Detecting cyber attacks in industrial control systems using convolutional neural networks. In: Proceedings of the 2018 Workshop on Cyber-Physical Systems Security and PrivaCy, pp. 72–83. ACM (2018)
[23]
Lee, E.A.: Cyber physical systems: Design challenges. Technical report UCB/EECS-2008-8, EECS Department, University of California, Berkeley, January 2008
[24]
Mathur, A., Tippenhauer, N.O.: SWaT: a water treatment testbed for research and training on ICS security. In: Proceedings of Workshop on Cyber-Physical Systems for Smart Water Networks (CySWater), April 2016
[25]
Melis, M., Demontis, A., Pintor, M., Sotgiu, A., Biggio, B.: secML: a Python library for secure and explainable machine learning. arXiv:1912.10013 (2019)
[26]
Pierazzi, F., Pendlebury, F., Cortellazzi, J., Cavallaro, L.: Intriguing properties of adversarial ML attacks in the problem space. In: Proceedings of the IEEE Symposium on Security and Privacy, pp. 1332–1349. IEEE (2020)
[27]
Shen, J., Won, J.Y., Chen, Z., Chen, Q.A.: Drift with devil: security of multi-sensor fusion based localization in high-level autonomous driving under GPS spoofing. In: Proceedings of the USENIX Security Symposium, pp. 931–948, August 2020
[28]
Taormina, R., Galelli, S.: A deep learning approach for the detection and localization of cyber-physical attacks on water distribution systems. J. Water Resourc. Plann. Manag. 144(10) (2018)
[29]
Urbina, D., et al.: Limiting the impact of stealthy attacks on industrial control systems. In: Proceedings of the ACM Conference on Computer and Communications Security (CCS), October 2016
[30]
Van Overschee P and De Moor B N4sid: subspace algorithms for the identification of combined deterministic-stochastic systems Automatica 1994 30 1 75-93
[31]
Weinberger S Computer security: is this the start of cyberwarfare? Nature 2011 174 142-145
[32]
Wikipedia, t.f.e.: Colonial pipeline ransomware attack. https://en.wikipedia.org/wiki/Colonial_Pipeline_ransomware_attack. Accessed 21 May 2022
[33]
Zizzo, G., Hankin, C., Maffeis, S., Jones, K.: Adversarial attacks on time-series intrusion detection for industrial control systems. In: IEEE TrustCom (2020)
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Published In
Jul 2023
280 pages
ISBN:978-3-031-35503-5
DOI:10.1007/978-3-031-35504-2
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 12 July 2023
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