Skip to main content
SpringerLink
Log in

A Lightweight Intrusion Detection and Electricity Theft Detection System for Smart Grid

  • Conference paper
  • First Online:
Information Security, Privacy and Digital Forensics (ICISPD 2022)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1075))

  • 141 Accesses

Abstract

Smart grid systems have improved networking for power systems and many other industrial systems, but they still have many vulnerabilities, making them an easy target for cyber attacks. Recently, the number of attacks has also increased. The present work investigates the reliability and security of Smart Grid (SG). The reliability and security are investigated in two aspects that are electricity fraud detection followed by the intrusion detection system. This work presents the lightweight Intrusion detection system for SCADA and Modbus-based control systems that can detect intrusion with very high accuracy. The IDS developed is based on the ICS (industrial control system) dataset, which has 20 features (column) and 2,74,628 rows. The IDS dataset contains the Modbus packet’s attributes and network and physical infrastructure attributes. The IDS work is followed by detecting electricity theft on a realistic electricity consumption dataset released by the State Grid Corporation of China. A total of 42,372 users’ power usage data from 1,035 days is included in the data collection (from 1 January 2014 to 31 October 2016). Eight classifiers, as well as two basic neural networks (1DCNN and ANN), have been investigated on this dataset.

Supported by C3iHub-IIT Kanpur, India.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. http://www.ece.uah.edu/thm0009/icsdatasets/IanArffDataset.arff

  2. Zheng Z, Yang Y, Niu X, Dai H-N, Zhou Y (2018) Wide and deep convolutional neural networks for electricity-theft detection to secure smart grids. IEEE Trans Ind Inf 14(4):1606–1615. https://doi.org/10.1109/TII.2017.2785963

    Article  Google Scholar 

  3. Cat Boost classifier: https://github.com/catboost/catboost

  4. Sayegh N, Elhajj IH, Kayssi A, Chehab A (2014) SCADA intrusion detection system based on temporal behavior of frequent patterns. In: MELECON 2014—17th IEEE mediterranean electrotechnical conference, pp 432-438. https://doi.org/10.1109/MELCON.2014.6820573

  5. Bulle BB, Santin AO, Viegas EK, dos Santos RR (2020) A host-based intrusion detection model based on OS diversity for SCADA. In: IECON 2020 the 46th annual conference of the IEEE industrial electronics society, pp 691–696. https://doi.org/10.1109/IECON43393.2020.9255062

  6. Singh VK, Ebrahem H, Govindarasu M (2018) Security evaluation of two intrusion detection systems in smart grid SCADA environment. In: North American power symposium (NAPS), pp 1–6. https://doi.org/10.1109/NAPS.2018.8600548

  7. Lopez Perez R, Adamsky F, Soua R, Engel T (2018) Machine learning for reliable network attack detection in SCADA systems. In: 2018 17th IEEE International conference on trust, security and privacy in computing and communications/12th IEEE international conference on big data science and engineering (TrustCom/BigDataSE), pp 633–638. https://doi.org/10.1109/TrustCom/BigDataSE.2018.00094

  8. Kumar A, Choi BJ (2022) Benchmarking machine learning based detection of cyber attacks for critical infrastructure. In: International conference on information networking (ICOIN), pp 24–29. https://doi.org/10.1109/ICOIN53446.2022.9687293

  9. This research employs a SCADA dataset

    Google Scholar 

  10. Yang H, Cheng L, Chuah MC (2019) Deep-learning-based network intrusion detection for SCADA systems. In: IEEE conference on communications and network security (CNS), pp 1–7. https://doi.org/10.1109/CNS.2019.8802785

  11. Altaha M, Lee JM, Aslam M, Hong S (2021) An autoencoder-based network intrusion detection system for the SCADA system

    Google Scholar 

  12. Zheng Z, Yang Y, Niu X, Dai H-N, Zhou Y (2018) Wide and deep convolutional neural networks for electricity-theft detection to secure smart grids. IEEE Trans Ind Inf 14(4):1606–1615. https://doi.org/10.1109/TII.2017.2785963

    Article  Google Scholar 

  13. Abdulaal MJ et al (2022) Real-time detection of false readings in smart grid AMI using deep and ensemble learning. IEEE Access 10:47541–47556. https://doi.org/10.1109/ACCESS.2022.3171262

    Article  Google Scholar 

  14. Lepolesa LJ, Achari S, Cheng L (2022) Electricity theft detection in smart grids based on deep neural network. IEEE Access 10:39638–39655. https://doi.org/10.1109/ACCESS.2022.3166146

    Article  Google Scholar 

  15. Alkuwari AN, Al-Kuwari S, Qaraqe M (2022) Anomaly detection in smart grids: a survey from cybersecurity perspective. In: 3rd International conference on smart grid and renewable energy (SGRE), pp 1–7. https://doi.org/10.1109/SGRE53517.2022.9774221

  16. Lee J, Sun YG, Sim I, Kim SH, Kim DI, Kim JY (2022) Non-technical loss detection using deep reinforcement learning for feature cost efficiency and imbalanced dataset. IEEE Access 10:27084–27095. https://doi.org/10.1109/ACCESS.2022.3156948

    Article  Google Scholar 

  17. Ullah A, Javaid N, Asif M, Javed MU, Yahaya AS (2022) AlexNet, adaboost and artificial bee colony based hybrid model for electricity theft detection in smart grids. IEEE Access 10:18681–18694. https://doi.org/10.1109/ACCESS.2022.3150016

    Article  Google Scholar 

  18. Xia X, Xiao Y, Liang W, Cui J (2022) Detection methods in smart meters for electricity thefts: a survey. Proc IEEE 110(2):273–319. https://doi.org/10.1109/JPROC.2021.3139754

    Article  Google Scholar 

  19. Zhao Q, Chang Z, Min G (2022) Anomaly detection and classification of household electricity data: a time window and multilayer hierarchical network approach. IEEE Internet Things J 9(5):3704–3716. https://doi.org/10.1109/JIOT.2021.3098735

    Article  Google Scholar 

  20. Althobaiti A, Jindal A, Marnerides AK, Roedig U (2021) Energy theft in smart grids: a survey on data-driven attack strategies and detection methods. IEEE Access 9:159291–159312. https://doi.org/10.1109/ACCESS.2021.3131220

    Article  Google Scholar 

  21. https://www.sciencedirect.com/science/article/pii/S2090447920301064

  22. https://accelconf.web.cern.ch/ica99/papers/mc1i01.pdf

  23. Reynders D, Mackay S, Wright E (2004) Modbus overview. Edwin PY-2004/12/31, SP-132, EP-141, SN-9780750663953, T1. https://doi.org/10.1016/B978-075066395-3/50012-7

  24. Ghosh S, Dasgupta A, Swetapadma A (2019) A study on support vector machine based linear and non-linear pattern classification. In: International conference on intelligent sustainable systems (ICISS), pp 24–28. https://doi.org/10.1109/ISS1.2019.8908018

  25. Huang M (2020) Theory and implementation of linear regression. In: 2020 International conference on computer vision, image and deep learning (CVIDL), pp 210–217. https://doi.org/10.1109/CVIDL51233.2020.00-99

  26. Ho TK (1995) Random decision forests. In: Proceedings of 3rd international conference on document analysis and recognition, vol 1, pp 278–282. https://doi.org/10.1109/ICDAR.1995.598994

  27. Navada A, Ansari AN, Patil S, Sonkamble BA (2011) Overview of use of decision tree algorithms in machine learning. IEEE Control and System Graduate Research Colloquium 2011:37–42. https://doi.org/10.1109/ICSGRC.2011.5991826

    Article  Google Scholar 

  28. Uhrig RE (1995) Introduction to artificial neural networks. In: Proceedings of IECON ’95—21st annual conference on IEEE industrial electronics, vol 1, pp 33–37. https://doi.org/10.1109/IECON.1995.483329

  29. Upadhyay D, Manero J, Zaman M, Sampalli S (2021) Gradient boosting feature selection with machine learning classifiers for intrusion detection on power grids. IEEE Trans Netw Serv Manag 18(1):1104–1116. https://doi.org/10.1109/TNSM.2020.3032618

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Indian Institute of Information Technology, Allahabad, Uttar Pradesh, India

    Ayush Sinha, Ashutosh Kaushik, Ranjana Vyas & O. P. Vyas

Authors
  1. Ayush Sinha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ashutosh Kaushik
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ranjana Vyas
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. O. P. Vyas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ayush Sinha .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, India

    Sankita J. Patel

  2. School of Cyber Security and Digital Forensics, National Forensic Sciences University, Gujarat, India

    Naveen Kumar Chaudhary

  3. Department of Computer Science and Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, India

    Bhavesh N. Gohil

  4. Florida International University, Miami, FL, USA

    S. S. Iyengar

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sinha, A., Kaushik, A., Vyas, R., Vyas, O.P. (2024). A Lightweight Intrusion Detection and Electricity Theft Detection System for Smart Grid. In: Patel, S.J., Chaudhary, N.K., Gohil, B.N., Iyengar, S.S. (eds) Information Security, Privacy and Digital Forensics. ICISPD 2022. Lecture Notes in Electrical Engineering, vol 1075. Springer, Singapore. https://doi.org/10.1007/978-981-99-5091-1_5

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-5091-1_5

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-5090-4

  • Online ISBN: 978-981-99-5091-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation