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Directions for Further Work

  • N. N. R. Ranga Suri
  • Narasimha Murty M
  • G. Athithan
Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 155)

Abstract

A summary of the important technical aspects presented in this book is furnished here for ready reference. It includes a few technically promising directions for future work in this field of research with respect to various emerging applications as well as the developments taking place on the computing front.

References

  1. 1.
    Du, M., Li, F., Zheng, G., Srikumar, V.: Deeplog: anomaly detection and diagnosis from system logs through deep learning. In: CCS, pp. 1285–1298. Dallas, TX, USA (2017)Google Scholar
  2. 2.
    Fontugne, R., Mazel, J., Fukuda, K.: Hashdoop: a mapreduce framework for network anomaly detection. In: IEEE INFOCOM Workshop on Security and Privacy in Big Data, pp. 494–499. IEEE (2014)Google Scholar
  3. 3.
    Golmohammadi, K., Zaiane, O.R.: Time series contextual anomaly detection for detecting market manipulation in stock market. In: DSAA. IEEE (2015)Google Scholar
  4. 4.
    Huang, T., Liu, C., Sharma, A., Sarkar, S.: Traffic system anomaly detection using spatiotemporal pattern networks. Int. J. Progn. Health Manag. 3 (2018)Google Scholar
  5. 5.
    Islam, S.R., Ghafoor, S.K., Eberle, W.: Mining illegal insider trading of stocks: a proactive approach (2018)Google Scholar
  6. 6.
    Leskovec, J.: Large-scale graph representation learning. In: IEEE International Conference on Big Data, p. 4. Boston, MA, USA (2017)Google Scholar
  7. 7.
    Li, X., Li, Z., Han, J., Lee, J.: Temporal outlier detection in vehicle traffic data. In: Proceedings of the 25th International Conference on Data Engineering ICDE, pp. 1319–1322. Shanghai, China (2009)Google Scholar
  8. 8.
    Mookiah, L., Dean, C., Eberle, W.: Graph-based anomaly detection on smart grid data. In: Proceedings of the Thirtieth International Florida Artificial Intelligence Research Society Conference FLAIRS, pp. 306–311 (2017)Google Scholar
  9. 9.
    Moriano, P., Rich, J.P.S., Camp, L.J.: Insider threat event detection in user-system interactions. In: MIST. Dallas, TX, USA (2017)Google Scholar
  10. 10.
    Narayanam, R., Suri, N.N.R.R., Garg, V.K., Murty, M.N.: Ranking mechanisms for maximizing spread, trust in signed social networks. In: ECML-PKDD Workshop: CoLISD (2012)Google Scholar
  11. 11.
    Paz, A., Plaza, A.: A new morphological anomaly detection algorithm for hyperspectral images and its GPU implementation. In: Huang, B., Plaza, A.J., Thiebaut, C. (eds.) Proceedings of SPIE, vol. 8157 (2011)Google Scholar
  12. 12.
    Razaq, A., Tianfield, H., Barrie, P.: A big data analytics based approach to anomaly detection. In: 3rd International Conference on Big Data Computing. Applications and Technologies BDCAT, pp. 187–193. ACM, Shanghai, China (2016)Google Scholar
  13. 13.
    Wang, H., Wen, H., Yi, F., Zhu, H., Sun, L.: Road traffic anomaly detection via collaborative path inference from GPS snippets. Sensors 17(550), 1–21 (2017)Google Scholar
  14. 14.
    Wei, Q., Ren, Y., Hou, R., Shi, B., Lo, J.Y., Carin, L.: Anomaly detection for medical images based on a one-class classification. In: Proceedings of SPIE Medical Imaging. Houston, Texas, US (2018)Google Scholar
  15. 15.
    Zhang, J., Wydrowski, R., Wang, Z., Arrabolu, S.S., Kanazawa, K., Gudalewicz, L., Gao, H., Batoukov, R., Aghajanyan, S., Tran, K.: Mbius: online anomaly detection and diagnosis. In: KDD. El London, UK (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • N. N. R. Ranga Suri
    • 1
  • Narasimha Murty M
    • 2
  • G. Athithan
    • 3
  1. 1.Centre for Artificial Intelligence and Robotics (CAIR)BangaloreIndia
  2. 2.Department of Computer Science and AutomationIndian Institute of Science (IISc)BangaloreIndia
  3. 3.Defence Research and Development Organization (DRDO)New DelhiIndia

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