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Research on Intrusion Detection Technology of Industrial Control Systems

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Artificial Intelligence and Security (ICAIS 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1254))

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Abstract

The industrial control system is the core of various infrastructures. With the development of process technology and the development of computer network technology, industrial control systems are constantly integrating with the Internet, evolving into an open system, which also brings numerous threats to the industrial control systems. As an important security protection technology, many scholars have conducted a lot of research on industrial control system intrusion detection. The main work of this paper is to summarize the current intrusion detection technology. First part introduces the industrial control system and analyzing its threat and the main defense technologies. The second introduces the intrusion detection technology. followed by the current research on the different classification methods of intrusion detection technology to summarize, classify and compare the existing research. Finally, it summarizes and looks forward to intrusion detection technology of industrial control system.

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References

  1. Langner, R.: Stuxnet: dissecting a cyberwarfare weapon. IEEE Secur. Priv. Mag. 9(3), 49–51 (2011)

    Article  Google Scholar 

  2. Bai, X.: Discussion on industrial control system security threat and protection application. China information technology (2018)

    Google Scholar 

  3. Tan, Q., Gao, Y., Shi, J., Wang, X., Fang, B., Tian, Z.: Toward a comprehensive insight to the eclipse attacks of tor hidden services. IEEE Internet Things J. 6(2), 1584–1593 (2019)

    Article  Google Scholar 

  4. Qi, W.: Review on information security of industrial control systems. Commun. Power Technol. 36(05), 225–226 (2019)

    Article  Google Scholar 

  5. Tian, Z., Li, M., Qiu, M., Sun, Y., Su, S.: Block-DEF: a secure digital evidence system using blockchain. Inf. Sci. 491, 151–165 (2019). https://doi.org/10.1016/j.ins.2019.04.011

    Article  Google Scholar 

  6. Tian, Z., Su, S., Yu, X., et al.: Vcash: a novel reputation framework for identifying denial of traffic service in internet of connected vehicles. IEEE Internet Things J. 7(5), 3901–3909 (2019)

    Article  Google Scholar 

  7. Tian, Z., et al.: Real time lateral movement detection based on evidence reasoning network for edge computing environment. IEEE Trans. Ind. Inform. 15(7), 4285–4294 (2019). https://doi.org/10.1109/TII.2019.2907754

    Article  Google Scholar 

  8. Tian, Z., Gao, X., Su, S., Qiu, J., Du, X., Guizani, M.: Evaluating reputation management schemes of Internet of vehicles based on evolutionary game theory. IEEE Trans. Veh. Technol. 68(6), 5971–5980 (2019). https://doi.org/10.1109/TVT.2019.2910217

    Article  Google Scholar 

  9. Misra, S., Krishna, P.V., Abraharm, K.I.: Energy efficient learning solution for intrusion detection in wireless sensor network. In: Proceedings of the 2nd Communication System and Networks, pp. 1–6. IEEE (2010)

    Google Scholar 

  10. Barbosa, R.R.R., Sadre, R., Pras, A.: Towards periodicity based anomaly detection in SCADA networks. In: Proceedings of 2012 IEEE 17th International Conference on Emerging Technologies and Factory Automation (ETFA 2012). IEEE (2012)

    Google Scholar 

  11. Hou, C., et al.: A probabilistic principal component analysis approach for detecting traffic normaly in industrial networks. J. Xi’an Jiaotong Univ. 46(2), 70–75 (2012)

    Google Scholar 

  12. Luo, Y.: Research and design on intrusion detection methods for industrial control system. Ph.D. Zhejiang University, Hangzhou, China (2013)

    Google Scholar 

  13. Ten, C.W., Hong, J., Liu, C.C.: Anomaly detection for cybersecurity of the substations. IEEE Trans. Smart Grid 2(4), 865–873 (2011)

    Article  Google Scholar 

  14. Morrist, T., Vaughnr, R., Dandassy, Y.: A retrofit network intrusion detection system for modbus RTU and ASCII industrial control systems. In: The 45th Hawaii International Conference on System Science, pp. 2338–2345 (2012)

    Google Scholar 

  15. Zhang, R., Chen, H.: SVPSO-SVM industrial control intrusion detection algorithm, pp. 1–17 (2019). Accessed 29 Nov 2019. https://doi.org/10.19678/j.issn.1000-3428.0054989

  16. Vollmer, T., Manic, M.: Cyber-physical system security with deceptive virtual hosts for industrial control networks. IEEE Trans. Ind. Inf. 10(2), 1337–1347 (2014)

    Article  Google Scholar 

  17. Tian, Z., Su, S., Shi, W., Du, X., Guizani, M., Yu, X.: A data-driven model for future Internet route decision modeling. Future Gener. Comput. Syst. 95, 212–220 (2019)

    Article  Google Scholar 

  18. Shang, W., et al.: Industrial communication intrusion detection algorithm based on improved one-class SVM. In: 2015 World Congress on Industrial Control Systems Security (WCICSS). IEEE (2015)

    Google Scholar 

  19. Xiong, Y., Wang, H.: Research on network intrusion detection based on SSAE-PNN algorithm 019. J. Tianjin Univ. Technol. 35(05), 6–11 (2015)

    Google Scholar 

  20. Morris, T., Vaughn, R., Dandass, Y.: A retrofit network intrusion detection system for MODBUS RTU and ASCII industrial control systems. In: 2012 45th Hawaii International Conference on System Sciences. IEEE (2012)

    Google Scholar 

  21. Wang, H.: On anomaly detection and defense resource allocation of industrial control networks. Diss. Zhejiang University (2014)

    Google Scholar 

  22. Hou, C.Y., Jiang, H.H., Rui, W.Z., Liu, L.: A probabilistic principal component analysis approach for detecting traffic anomaly in industrial network. J. Xi’an Jiao Tong Univ. 46(2), 70–75 (2017)

    Google Scholar 

  23. Gao, C.M.: Network traffic anomaly detection based on industrial control network. Beijing University of Technology, Beijing (2014)

    Google Scholar 

  24. Shang, W.L., Sheng, S.Z., Ming, W.: Modbus/TCP communication anomaly detection based on PSO-SVM. In: Applied Mechanics and Materials. vol. 490. Trans Tech Publications (2014)

    Google Scholar 

  25. Yang, D., Usynin, A., Hines, J.W.: Anomaly-based intrusion detection for SCADA systems. In: 5th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human Machine Interface Technologies (Npic & Hmit 2005) (2015)

    Google Scholar 

  26. Liu, C.C.: Research on intrusion detection technology of industrial control system. University of Electronic Science and Technology of China, Cheng Du (2017)

    Google Scholar 

  27. Khalili, A., Sami, A.: SysDetect: a systematic approach to critical state determination for industrial intrusion detection systems using Apriori algorithm. J. Process Control 32, 154–160 (2015)

    Article  Google Scholar 

  28. Ahmed, S., et al.: Unsupervised machine learning—based detection of covert data integrity assault in smart grid networks utilizing isolation forest. IEEE Trans. Inf. Forensics Secur. 14(10), 2765–2777 (2019)

    Article  Google Scholar 

  29. Huazhong, W., Zhihui, Y., et al.: Application of fusion PCA and PSO-SVM method in industrial control intrusion detection. Bull. Sci. Technol. 33(1), 80–85 (2017)

    Google Scholar 

  30. Zhang, F., et al.: Multi-layer data-driven cyber-attack detection system for industrial control systems based on network, system and process data. IEEE Trans. Ind. Inf. 15(7), 4362–4369 (2019)

    Article  Google Scholar 

  31. Erez, N., Avishai, W.: Control variable classification, modeling and anomaly detection in Modbus/TCP SCADA systems. Int. J. Crit. Infrastruct. Prot. 10, 59–70 (2015)

    Article  Google Scholar 

Download references

Acknowledgments

This work is funded by the National Key Research and Development Plan (Grant No. 2018YFB0803504), the National Natural Science Foundation of China (No. 61702223, 61702220, 61871140, 61602210, 61877029, U1636215), the Science and Technology Planning Project of Guangdong (2017A040405029), the Science and Technology Planning Project of Guangzhou (201902010041), the Fundamental Research Funds for the Central Universities (21617408, 21619404).

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Authors and Affiliations

  1. Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China

    Dailing Xia & Yanbin Sun

  2. Jinan University, Guangzhou, China

    Quanlong Guan

Authors
  1. Dailing Xia
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  2. Yanbin Sun
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  3. Quanlong Guan
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Corresponding author

Correspondence to Yanbin Sun .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Jinwei Wang

  3. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Xia, D., Sun, Y., Guan, Q. (2020). Research on Intrusion Detection Technology of Industrial Control Systems. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Communications in Computer and Information Science, vol 1254. Springer, Singapore. https://doi.org/10.1007/978-981-15-8101-4_13

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  • DOI: https://doi.org/10.1007/978-981-15-8101-4_13

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-8100-7

  • Online ISBN: 978-981-15-8101-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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