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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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)
Golmohammadi, K., Zaiane, O.R.: Time series contextual anomaly detection for detecting market manipulation in stock market. In: DSAA. IEEE (2015)
Huang, T., Liu, C., Sharma, A., Sarkar, S.: Traffic system anomaly detection using spatiotemporal pattern networks. Int. J. Progn. Health Manag. 3 (2018)
Islam, S.R., Ghafoor, S.K., Eberle, W.: Mining illegal insider trading of stocks: a proactive approach (2018)
Leskovec, J.: Large-scale graph representation learning. In: IEEE International Conference on Big Data, p. 4. Boston, MA, USA (2017)
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)
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)
Moriano, P., Rich, J.P.S., Camp, L.J.: Insider threat event detection in user-system interactions. In: MIST. Dallas, TX, USA (2017)
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)
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)
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)
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)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Ranga Suri, N.N.R., Murty M, N., Athithan, G. (2019). Directions for Further Work. In: Outlier Detection: Techniques and Applications. Intelligent Systems Reference Library, vol 155. Springer, Cham. https://doi.org/10.1007/978-3-030-05127-3_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-05127-3_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05125-9
Online ISBN: 978-3-030-05127-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)