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Results for Anomaly detection

Publications & Outputs

  1. STGAT-MAD: Spatial-Temporal Graph Attention Network for Multivariate Time Series Anomaly Detection

    Zhan, J., Wang, S., Ma, X., Wu, C., Yang, C., Zeng, D. & Wang, S., 27/04/2022, (ICASSP 2022) 2022 IEEE International Conference on Acoustics, Speech and Signal Processing, May 22-27, 2022, Singapore. IEEE, 5 p.

    Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

  2. Innovative and Additive Outlier Robust Kalman Filtering with a Robust Particle Filter

    Fisch, A., Eckley, I. & Fearnhead, P., 31/01/2022, In: IEEE Transactions on Signal Processing. 70, p. 47-56 10 p.

    Research output: Contribution to Journal/MagazineJournal articlepeer-review

  3. An evolving approach to data streams clustering based on typicality and eccentricity data analytics

    Bezerra, C. G., Costa, B. S. J., Guedes, L. A. & Angelov, P. P., 31/05/2020, In: Information Sciences. 518, p. 13-28 16 p.

    Research output: Contribution to Journal/MagazineJournal articlepeer-review

  4. Robust Functional Regression for Outlier Detection

    Hullait, H., Leslie, D. S., Pavlidis, N. G. & King, S., 23/01/2020, Advanced Analytics and Learning on Temporal Data: 4th ECML PKDD Workshop, AALTD 2019, Würzburg, Germany, September 20, 2019, Revised Selected Papers. Lemaire, V., Malinowski, S., Bagnall, A., Bondu, A., Guyet, T. & Tavenard, R. (eds.). Cham: Springer, p. 3-13 11 p. (Lecture Notes in Computer Science; vol. 11986).

    Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

  5. Hybrid self-organizing feature map (SOM) for anomaly detection in cloud infrastructures using granular clustering based upon value-difference metrics

    Stephanakis, I. M., Chochliouros, I. P., Sfakianakis, E., Shirazi, S. N. & Hutchison, D., 1/08/2019, In: Information Sciences. 494, p. 247-277 31 p.

    Research output: Contribution to Journal/MagazineJournal articlepeer-review

  6. Automatic detection of computer network traffic anomalies based on eccentricity analysis

    Martins, R. S., Angelov, P. & Costa, B. S. J., 15/10/2018, 2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 8 p. 8491507

    Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

  7. Anomalous behaviour detection based on heterogeneous data and data fusion

    Mohd Ali, A. & Angelov, P., 05/2018, In: Soft Computing. 22, 10, p. 3187-3201 15 p.

    Research output: Contribution to Journal/MagazineJournal articlepeer-review

  8. Real-time Recognition of Calling Pattern and Behaviour of Mobile Phone users through Anomaly Detection and Dynamically Evolving Clustering

    Iglesias, J. A., Ledezma, A., Sanchis, A. & Angelov, P. P., 5/08/2017, In: Applied Sciences. 7, 8, p. 1-14 14 p., 798.

    Research output: Contribution to Journal/MagazineJournal articlepeer-review

  9. Detecting anomalous behaviour using heterogeneous data

    Mohd Ali, A., Angelov, P. P. & Gu, X., 7/09/2016, Advances in Computational Intelligence Systems: Contributions Presented at the 16th UK Workshop on Computational Intelligence, September 7–9, 2016, Lancaster, UK. Angelov, P., Gegov, A., Jayne, C. & Shen, Q. (eds.). Springer, p. 253-276 24 p. (Advances in Intelligent Systems and Computing; vol. 513).

    Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

  10. Traffic anomaly diagnosis in Internet backbone networks: a survey

    Marnerides, A., Schaeffer-Filho, A. & Mauthe, A., 14/11/2014, In: Computer Networks. 73, p. 224-243 20 p.

    Research output: Contribution to Journal/MagazineJournal articlepeer-review