NIPAD: a non-invasive power-based anomaly detection scheme for programmable logic controllers

  • Yu-jun Xiao
  • Wen-yuan XuEmail author
  • Zhen-hua Jia
  • Zhuo-ran Ma
  • Dong-lian Qi


Industrial control systems (ICSs) are widely used in critical infrastructures, making them popular targets for attacks to cause catastrophic physical damage. As one of the most critical components in ICSs, the programmable logic controller (PLC) controls the actuators directly. A PLC executing a malicious program can cause significant property loss or even casualties. The number of attacks targeted at PLCs has increased noticeably over the last few years, exposing the vulnerability of the PLC and the importance of PLC protection. Unfortunately, PLCs cannot be protected by traditional intrusion detection systems or antivirus software. Thus, an effective method for PLC protection is yet to be designed. Motivated by these concerns, we propose a non-invasive powerbased anomaly detection scheme for PLCs. The basic idea is to detect malicious software execution in a PLC through analyzing its power consumption, which is measured by inserting a shunt resistor in series with the CPU in a PLC while it is executing instructions. To analyze the power measurements, we extract a discriminative feature set from the power trace, and then train a long short-term memory (LSTM) neural network with the features of normal samples to predict the next time step of a normal sample. Finally, an abnormal sample is identified through comparing the predicted sample and the actual sample. The advantages of our method are that it requires no software modification on the original system and is able to detect unknown attacks effectively. The method is evaluated on a lab testbed, and for a trojan attack whose difference from the normal program is around 0.63%, the detection accuracy reaches 99.83%.

Key words

Industrial control system Programmable logic controller Side-channel Anomaly detection Long short-term memory neural networks 

CLC number



Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Alcaraz, C., Zeadally, S., 2013. Critical control system protection in the 21st century. Computer, 46(10): 74–83. Scholar
  2. Alcaraz, C., Zeadally, S., 2015. Critical infrastructure protection: requirements and challenges for the 21st century. Int. J. Crit. Infrastr. Protect., 8: 53–66. Scholar
  3. Bencsáth, B., Pék, G., Buttyán, L., et al., 2012. The cousins of Stuxnet: Duqu, Flame, and Gauss. Fut. Int., 4(4): 971–1003. Scholar
  4. Bolton, W., 2015. Programmable Logic Controllers (6th Ed.). Newnes, USA.CrossRefGoogle Scholar
  5. Bullock, J., Conservatoire, U.C.E.B., 2007. LibXtract: a lightweight library for audio feature extraction. Proc. Int. Computer Music Conf., p.1–4.Google Scholar
  6. Candes, E.J., Tao, T., 2006. Near-optimal signal recovery from random projections: universal encoding strategies? IEEE Trans. Inform. Theory, 52(12): 5406–5425. Scholar
  7. Cárdenas, A.A., Amin, S., Sastry, S., 2008. Research challenges for the security of control systems. Proc. 3rd Conf. on Hot Topics in Security, Article 6.Google Scholar
  8. Chen, T.M., Abu-Nimeh, S., 2011. Lessons from Stuxnet. Computer, 44(4): 91–93. Scholar
  9. Clark, S.S., Ransford, B., Rahmati, A., et al., 2013. WattsUpDoc: power side channels to nonintrusively discover untargeted malware on embedded medical devices. Proc. USENIX Workshop on Health Information Technologies, p.1–11.Google Scholar
  10. Coletta, A., Armando, A., 2015. Security monitoring for industrial control systems. Proc. Conf. on Cybersecurity of Industrial Control Systems, p.48–62. Scholar
  11. Dalal, N., Triggs, B., 2005. Histograms of oriented gradients for human detection. Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, p.886–893. Scholar
  12. Formby, D., Srinivasan, P., Leonard, A., et al., 2016. Who’s in control of your control system? Device fingerprinting for cyber-physical systems. Proc. Network and Distributed System Security Symp., p.1–13.Google Scholar
  13. García-Teodoro, P., Díaz-Verdejo, J., Maciá-Fernández, G., et al., 2009. Anomaly-based network intrusion detection: techniques, systems and challenges. Comput. Secur., 28(1-2):18–28. Scholar
  14. Gers, F.A., Schmidhuber, J.A., Cummins, F., 2000. Learning to forget: continual prediction with LSTM. Neur. Comput., 12(10): 2451–2471. Scholar
  15. Gonzalez, C.A., Hinton, A., 2014. Detecting malicious software execution in programmable logic controllers using power fingerprinting. Proc. Int. Conf. on Critical Infrastructure Protection, p.15–27. Scholar
  16. Johnson, R.E., 2010. Survey of SCADA security challenges and potential attack vectors. Proc. Int. Conf. for Internet Technology and Secured Transactions, p.1–5.Google Scholar
  17. Kesler, B., 2011. The vulnerability of nuclear facilities to cyber attack. Strat. Insights, 10(1): 15–25.Google Scholar
  18. Krotofil, M., Gollmann, D., 2013. Industrial control systems security: what is happening? Proc. 11th IEEE Int. Conf. on Industrial Informatics, p.670–675. Scholar
  19. Langner, R., 2011. Stuxnet: dissecting a cyberwarfare weapon. IEEE Secur. Priv. 9(3): 49–51. Scholar
  20. Lee, H., Battle, A., Raina, R., et al., 2006. Efficient sparse coding algorithms. Proc. 19th Int. Conf. on Neural Information Processing Systems, p.801–808.Google Scholar
  21. Lowe, D.G., 2004. Distinctive image features from scaleinvariant keypoints. Int. J. Comput. Vis., 60(2): 91–110. Scholar
  22. Macaulay, T., Singer, B.L., 2011. Cybersecurity for Industrial Control Systems: SCADA, DCS, PLC, HMI, and SIS. CRC Press, USA.CrossRefGoogle Scholar
  23. Malhotra, P., Vig, L., Shroff, G., et al., 2015. Long short term memory networks for anomaly detection in time series. Proc. European Symp. on Artificial Neural Networks, Computational Intelligence and Maching Learning, p.89–94.Google Scholar
  24. Manevitz, L.M., Yousef, M., 2002. One-class SVMs for document classification. J. Mach. Learn. Res., 2: 139–154.zbMATHGoogle Scholar
  25. Mantere, M., Uusitalo, I., Sailio, M., et al., 2012. Challenges of machine learning based monitoring for industrial control system networks. Proc. 26th Int. Conf. on Advanced Information Networking and Applications Workshops, p.968–972. Scholar
  26. Morris, T., Vaughn, R., Dandass, Y., 2012. A retrofit network intrusion detection system for MODBUS RTU and ASCII industrial control systems. Proc. 45th Hawaii Int. Conf. on System Science, p.2338–2345. Scholar
  27. Nandakumar, K., Jain, A.K., 2004. Local correlation-based fingerprint matching. Proc. ICVGIP, p.503–508.Google Scholar
  28. Ni, B., Moulin, P., Yang, X., et al., 2015. Motion part regularization: improving action recognition via trajectory group selection. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, p.3698–3706. Scholar
  29. Pearson, K., 1901. Mathematical contributions to the theory of evolution. X. Supplement to a memoir on skew variation. Phil. Trans. R. Soc. A, 197: 443–459.zbMATHGoogle Scholar
  30. Peng, Y., Xiang, C., Gao, H., et al., 2015. Industrial control system fingerprinting and anomaly detection. Proc. Int. Conf. on Critical Infrastructure Protection, p.73–85. Scholar
  31. Piggin, R., 2015. Are industrial control systems ready for the cloud? Int. J. Crit. Infrastr. Protect., 9(C):38–40. Scholar
  32. Ponomarev, S., Atkison, T., 2016. Industrial control system network intrusion detection by telemetry analysis. IEEE Trans. Depend. Sec. Comput., 13(2): 252–260. Scholar
  33. Pretorius, B., van Niekerk, B., 2016. Cyber-security for ICS/SCADA: a South African perspective. Int. J. Cyber Warf. Terror., 6(3): 1–16. Scholar
  34. Shang, W., Zeng, P., Wan, M., et al., 2016. Intrusion detection algorithm based on OCSVM in industrial control system. Secur. Commun. Netw., 9(10): 1040–1049. Scholar
  35. Slay, J., Miller, M., 2007. Lessons learned from the Maroochy water breach. Proc. Int. Conf. on Critical Infrastructure Protection, p.73–82. Scholar
  36. Stone, S.J., Temple, M.A., Baldwin, R.O., 2015. Detecting anomalous programmable logic controller behavior using RF-based Hilbert transform features and a correlation-based verification process. Int. J. Crit. Infrastr. Protect., 9(C):41–51. Scholar
  37. Stouffer, K.A., Falco, J.A., Scarfone, K.A., 2011. Guide to Industrial Control Systems (ICS) Security: Supervisory Control and Data Acquisition (SCADA) Systems, Distributed Control Systems (DCS), and Other Control System Configurations such as Programmable Logic Controllers (PLC). Technical Report SP800-82, National Institute of Standards and Technology, USA.Google Scholar
  38. Wang, H., Kläser, A., Schmid, C., et al., 2013. Dense trajectories and motion boundary descriptors for action recognition. Int. J. Comput. Vis., 103(1): 60–79. Scholar
  39. Xu, J., Yang, G., Man, H., et al., 2013. L1 graph based on sparse coding for feature selection. Proc. Int. Symp. on Neural Networks, p.594–601. Scholar
  40. Zhong, W., Lu, H., Yang, M., 2012. Robust object tracking via sparsity-based collaborative model. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, p.1838–1845. Scholar

Copyright information

© Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  • Yu-jun Xiao
    • 1
  • Wen-yuan Xu
    • 1
    Email author
  • Zhen-hua Jia
    • 2
  • Zhuo-ran Ma
    • 1
  • Dong-lian Qi
    • 1
  1. 1.School of Electrical EngineeringZhejiang UniversityHangzhouChina
  2. 2.Wireless Information Network LaboratoryRutgers UniversityNorth BrunswickUSA

Personalised recommendations