Abstract
In this chapter, the algorithm summary of the proposed autonomous anomaly detection (AAD) algorithm described in Chap. 6 is provided. Numerical examples based on both the synthetic and benchmark datasets are presented for evaluating the performance of the AAD algorithm. Well-known traditional anomaly detection approaches are used for a further comparison. It was demonstrated through the numerical experiments that the AAD algorithm is able to provide a more objective, accurate way for anomaly detection, and its performance is not influenced by the structure of the data and is equally effective in detecting collective anomalies as well as individual anomalies. The pseudo-code of the main procedure of the AAD algorithm and the MATLAB implementation can be found in Appendices B.1 and C.1, respectively.
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
X. Gu, P. Angelov, in Autonomous Anomaly Detection, in IEEE Conference on Evolving and Adaptive Intelligent Systems (2017), pp. 1–8
H.T. Kahraman, S. Sagiroglu, I. Colak, The development of intuitive knowledge classifier and the modeling of domain dependent data. Knowl. Based Syst. 37, 283–295 (2013)
https://archive.ics.uci.edu/ml/datasets/User+Knowledge+Modeling
P. Cortez, A. Cerdeira, F. Almeida, T. Matos, J. Reis, Modeling wine preferences by data mining from physicochemical properties. Decis. Support Syst. 47, 547–553 (2009)
B.A. Johnson, R. Tateishi, N.T. Hoan, A hybrid pansharpening approach and multiscale object-based image analysis for mapping diseased pine and oak trees. Int. J. Remote Sens. 34(20), 6969–6982 (2013)
D.E. Denning, An intrusion-detection model, IEEE Trans. Softw. Eng., SE-13(2), 222–232, 1987
P.P. Angelov, in Anomaly Detection Based on Eccentricity Analysis, 2014 IEEE Symposium Series in Computational Intelligence, IEEE Symposium on Evolving and Autonomous Learning Systems, EALS, SSCI 2014, 2014, pp. 1–8
C. Thomas, N. Balakrishnan, Improvement in intrusion detection with advances in sensor fusion. IEEE Trans. Inf. Forensics Secur. 4(3), 542–551 (2009)
H. Moonesinghe, P. Tan, in Outlier detection using random walks, Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI’06), 2006, pp. 532–539
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Angelov, P.P., Gu, X. (2019). Applications of Autonomous Anomaly Detection. In: Empirical Approach to Machine Learning. Studies in Computational Intelligence, vol 800. Springer, Cham. https://doi.org/10.1007/978-3-030-02384-3_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-02384-3_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02383-6
Online ISBN: 978-3-030-02384-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)