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Distributed Generative Adversarial Networks for Anomaly Detection

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Decision and Game Theory for Security (GameSec 2020)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 12513))

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Abstract

Cognitive radio networks can be used to detect anomalous and adversarial communications to achieve situational awareness on the radio frequency spectrum. This paper proposes a distributed anomaly detection scheme based on adversarially-trained data models. While many anomaly detection methods typically depend on a central decision-making server, our distributed approach makes better use of decentralized resources, and decreases reliance on a single point of failure. Using a novel combination of generative adversarial network (GAN) elements, participating cognitive radio devices learn a representation of local network activity data through a non-cooperative (strategic) game. Deviations from this expected network activity are flagged as anomalies and treated as possible network security threats, improving situational awareness. Tested on a range of time series datasets, the performance of the proposed distributed scheme matches that of state-of-the-art, centralized anomaly detection methods.

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References

  1. Mitola, J.: An Integrated Agent Architecture for Software Defined Radio. Ph.D. Dissertation, KTH, July 2000

    Google Scholar 

  2. Shi, Y., Erpek, T., Sagduyu, Y.E., Li, J.H.: Spectrum Data Poisoning with Adversarial Deep Learning. arXiv:1901.09247 [cs], January 2019

  3. Chen, R., Park, J.-M., Reed, J.H.: Defense against primary user emulation attacks in cognitive radio networks. IEEE J. Sel. Areas Commun. 26(1), 25–37 (2008)

    Article  Google Scholar 

  4. Luo, Z., Lou, C., Chen, S., Zheng, S., Li, S.: Specific primary user sensing for wireless security in IEEE 802.22 network. In: 2011 11th International Symposium on Communications Information Technologies (ISCIT), pp. 18–22, October 2011

    Google Scholar 

  5. Srinivasan, S., Shivakumar, K.B., Mohammad, M.: Semi-supervised machine learning for primary user emulation attack detection and prevention through core-based analytics for cognitive radio networks. Int. J. Distrib. Sens. Netw. 15(9) (2019). https://doi.org/10.1177/1550147719860365

  6. Pu, D., Shi, Y., Ilyashenko, A.V., Wyglinski, A.M.: Detecting primary user emulation attack in cognitive radio networks. In: 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011, pp. 1–5, December 2011. ISSN: 1930–529X

    Google Scholar 

  7. Zhao, Y., Nasrullah, Z., Li, Z.: PyOD: A Python Toolbox for Scalable Outlier Detection. 20:7 (2019)

    Google Scholar 

  8. Gupta, C., et al.: ProtoNN: compressed and accurate kNN for resource-scarce devices. In: International Conference on Machine Learning, pp. 1331–1340. PMLR, July 2017. ISSN: 2640–3498

    Google Scholar 

  9. Lee, Y.J., Yeh, Y.R., Wang, Y.C.F.: Anomaly detection via online oversampling principal component analysis. IEEE Trans. Knowl. Data Eng. 25(7), 1460–1470 (2013)

    Article  Google Scholar 

  10. Li, D., Chen, D., Jin, B., Shi, L., Goh, J., Ng, S.-K.: MAD-GAN: Multivariate anomaly detection for time series data with generative adversarial networks. In: Tetko, I.V., Kůrková, V., Karpov, P., Theis, F. (eds.) ICANN 2019. LNCS, vol. 11730, pp. 703–716. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30490-4_56. arXiv:1901.04997

    Chapter  Google Scholar 

  11. Schlegl, T., Seeböck, P., Waldstein, S.M., Schmidt-Erfurth, U., Langs, G.: Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 146–157. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59050-9_12. arXiv:1703.05921

    Chapter  Google Scholar 

  12. Zenati, H., Foo, C.S., Lecouat, B., Manek, G., Chandrasekhar, V.R.: Efficient GAN-Based Anomaly Detection. arXiv:1802.06222 [cs, stat], May 2019

  13. Schlegl, T., Seebock, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks. Med. Image Anal. 54, 30–44 (2019)

    Article  Google Scholar 

  14. Liu, Y., et al.: Generative adversarial active learning for unsupervised outlier detection. IEEE Trans. Knowl. Data Eng. 32, 1517–1528 (2019). Conference Name: IEEE Transactions on Knowledge and Data Engineering

    Google Scholar 

  15. Hardy, C., Le Merrer, E., Sericola, B.: MD-GAN: Multi-Discriminator Generative Adversarial Networks for Distributed Datasets. arXiv:1811.03850 [cs, stat], February 2019

  16. Ferdowsi, A., Saad, W.: Generative Adversarial Networks for Distributed Intrusion Detection in the Internet of Things. arXiv:1906.00567 [cs, stat], June 2019

  17. Im, D.J., Ma, H., Kim, C.D., Taylor, G.: Generative Adversarial Parallelization. arXiv:1612.04021 [cs, stat], December 2016

  18. Hoang, Q., Nguyen, T.D., Le, T., Phung, D.: MGAN: Training generative adversarial nets with multiple generators, p. 24 (2018)

    Google Scholar 

  19. Goodfellow, I.J., et al.: Generative Adversarial Networks. arXiv:1406.2661 [cs, stat], June 2014

  20. Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: International Conference on Machine Learning, pp. 214–223, July 2017

    Google Scholar 

  21. Creswell, A., Bharath, A.A.: Inverting The Generator Of A Generative Adversarial Network (II). arXiv:1802.05701 [cs], February 2018

  22. Donahue, J., Krähenbühl, P., Darrell, T.: Adversarial Feature Learning. arXiv:1605.09782 [cs, stat], April 2017

  23. Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium. arXiv:1706.08500 [cs, stat], June 2017

  24. Thierens, D.: Non-redundant genetic coding of neural networks. In: IEEE International Conference on Evolutionary Computation, pp. 571–575, May 1996

    Google Scholar 

  25. Lucic, M., Kurach, K., Michalski, M., Gelly, S., Bousquet, O.: Are GANs Created Equal? A Large-Scale Study. arXiv:1711.10337 [cs, stat], October 2018

  26. Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. arXiv:1703.10593 [cs], November 2018

  27. Li, C., et al.: ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching. arXiv:1709.01215 [cs, stat], November 2017

  28. Weerasinghe, S.: https://github.com/sandamal/omnet_simulation. Accessed 10 June 2020

  29. The UCI KDD Archive. KDD Cup 1999 Data, October 1999

    Google Scholar 

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Acknowledgements

This work was supported in part by the Australian Research Council Linkage Project under the grant LP190101287, and by Northrop Grumman Mission Systems’ University Research Program.

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Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, Australia

    Marc Katzef & Tansu Alpcan

  2. School of Computing and Information Systems, University of Melbourne, Melbourne, Australia

    Andrew C. Cullen & Christopher Leckie

  3. Northrop Grumman Corporation, Denver, USA

    Justin Kopacz

Authors
  1. Marc Katzef
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  2. Andrew C. Cullen
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  3. Tansu Alpcan
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  4. Christopher Leckie
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  5. Justin Kopacz
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Corresponding author

Correspondence to Marc Katzef .

Editor information

Editors and Affiliations

  1. Tandon School of Engineering, New York University, Brooklyn, NY, USA

    Quanyan Zhu

  2. ISR, University of Maryland, College Park, MD, USA

    John S. Baras

  3. Electrical Engineering, University of Washington, Seattle, WA, USA

    Radha Poovendran

  4. New York University, New York, NY, USA

    Juntao Chen

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Katzef, M., Cullen, A.C., Alpcan, T., Leckie, C., Kopacz, J. (2020). Distributed Generative Adversarial Networks for Anomaly Detection. In: Zhu, Q., Baras, J.S., Poovendran, R., Chen, J. (eds) Decision and Game Theory for Security. GameSec 2020. Lecture Notes in Computer Science(), vol 12513. Springer, Cham. https://doi.org/10.1007/978-3-030-64793-3_1

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  • DOI: https://doi.org/10.1007/978-3-030-64793-3_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64792-6

  • Online ISBN: 978-3-030-64793-3

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