Skip to main content

Optimization of the Novelty Detection Model Based on LSTM Autoencoder for ICS Environment

  • Conference paper
  • First Online:
Intelligent Systems Applications in Software Engineering (CoMeSySo 2019 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1046))

Included in the following conference series:

Abstract

The recent evolution in cybersecurity shows how vulnerable our technology is. In addition, contemporary society becoming more reliant on “vulnerable technology”. This is especially relevant in case of critical information infrastructure, which is vital to retain the functionality of modern society. Furthermore, the cyber-physical systems as Industrial control systems are an essential part of critical information infrastructure; and therefore, need to be protected. This article presents a comprehensive optimization methodology in the field of industrial network anomaly detection. We introduce a recurrent neural network preparation for a one-class classification task. In order to optimize the recurrent neural network, we adopted a genetic algorithm. The main goal is to create a robust predictive model in an unsupervised manner. Therefore, we use hyperparameter optimization according to the validation loss function, which defines how well the machine learning algorithm models the given data. To achieve this goal, we adopted multiple techniques as data preprocessing, feature reduction, genetic algorithm, etc.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight 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. Chen, C.L.P., Zhang, C.-Y.: Data-intensive applications, challenges, techniques and technologies: a survey on Big Data. Inf. Sci. 275, 314–347 (2014)

    Article  Google Scholar 

  2. Knapp, E.D., Langill, J.T.: Industrial Network Security: Securing Critical Infrastructure Networks for Smart Grid, SCADA, and Other Industrial Control Systems. Syngress, Waltham (2014)

    Google Scholar 

  3. Maglaras, L.A., et al.: Cyber security of critical infrastructures. ICT Express 4(1), 42–45 (2018)

    Article  Google Scholar 

  4. Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. (CSUR) 41(3), 15 (2009)

    Article  Google Scholar 

  5. Markou, M., Singh, S.: Novelty detection: a review—part 1: statistical approaches. Sig. Process. 83(12), 2481–2497 (2003)

    Article  Google Scholar 

  6. Marchi, E., et al.: A novel approach for automatic acoustic novelty detection using a denoising autoencoder with bidirectional LSTM neural networks. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1996–2000. IEEE (2015)

    Google Scholar 

  7. Park, D., Hoshi, Y., Kemp, C.C.: A multimodal anomaly detector for robot-assisted feeding using an LSTM-based variational autoencoder. IEEE Robot. Autom. Lett. 3(3), 1544–1551 (2018)

    Article  Google Scholar 

  8. Malhotra, P., et al.: LSTM-based encoder-decoder for multi-sensor anomaly detection. arXiv preprint arXiv:1607.00148 (2016)

  9. Kiran, B., Thomas, D., Parakkal, R.: An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos. J. Imaging 4(2), 36 (2018)

    Article  Google Scholar 

  10. Lipton, Z.C., Berkowitz, J., Elkan, C.: A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019 (2015)

  11. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  12. D’errico, F., et al. (eds.): Conflict and Multimodal Communication: Social Research and Machine Intelligence. Springer, Heidelberg (2015)

    Google Scholar 

  13. Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis. Holland, JH (1975)

    MATH  Google Scholar 

  14. Lemay, A., Fernandez, J.M.: Providing {SCADA} network data sets for intrusion detection research. In: 9th Workshop on Cyber Security Experimentation and Test, CSET 2016 (2016)

    Google Scholar 

Download references

Acknowledgment

This work was funded by the Internal Grant Agency (IGA/FAI/2019/002) and supported by the research project VI20172019054 “An analytical software module for the real-time resilience evaluation from point of the converged security”, supported by the Ministry of the Interior of the Czech Republic in the years 2017–2019. Finally, we thank Lemay and Fernandez [14] who provides ICS datasets.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jan Vavra .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Vavra, J., Hromada, M. (2019). Optimization of the Novelty Detection Model Based on LSTM Autoencoder for ICS Environment. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Intelligent Systems Applications in Software Engineering. CoMeSySo 2019 2019. Advances in Intelligent Systems and Computing, vol 1046. Springer, Cham. https://doi.org/10.1007/978-3-030-30329-7_28

Download citation

Publish with us

Policies and ethics