Abstract
The article describes the results of the development of an algorithm for detecting anomalies of time series taking into account the features of the subject area. The algorithm involves finding a forecast of time series using recurrent neural networks, detecting anomalies according to the obtained forecast, filtering the detected anomalies in accordance with possible deviations of the time series values from the trend reflected in ontology, and logical output of search results using a set of rules. The effectiveness of the proposed approach is confirmed by a number of experiments conducted on the benchmark of data on the operation of oil rigs.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
REFERENCES
S. Ahmad and S. Purdy, “Real-time anomaly detection for streaming analytics,” (2016). https://doi.org/10.48550/ar**v.1607.02480
E. Busseti, I. Osband, and S. Wong, “Deep learning for time series modeling,” in CS 229 Final Project Report (Stanford Univ., Stanford, Calif., 2012). http://cs229.stanford.edu/proj2012/BussetiOsbandWong-DeepLearningForTimeSeriesModeling.pdf
V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: A survey,” ACM Comput. Surv. 41 (3), 15 (2007). https://doi.org/10.1145/1541880.1541882
K. Das, “Detecting patterns of anomalies,” PhD Thesis (Carnegie Mellon Univ., Pittsburg, Pa., 2009).
V. I. Hasanov, “Detection of anomalies in network traffic based on neural network modeling of dynamics of changes in IP packet volumes,” MMC, No. 2 (2018).
L. Jean-Baptiste, Ontologies with Python: Programming OWL 2.0 Ontologies with Pyton and Owlready2 (Apress, Berkeley, Cailf., 2021). https://doi.org/10.1007/978-1-4842-6552-9
A. Krizhevsky, in Convolutional Deep Belief Networks on CIFAR-10 (Univ. of Toronto, Toronto, 2010).
A. B. Lindigrin, “Analysis of specifics and problems of anomaly search processes in network data,” Izv. Tulskogo Gos. Univ. Tekh. Nauki, No. 5, 304–309 (2021). https://doi.org/10.24412/2071-6168-2021-5-304-309
X. Liu and P. S. Nielsen, “Regression-based online anomaly detection for smart grid data,” (2016). https://doi.org/10.48550/ar**v.1606.05781
P. Malhotra, L. Vig, G. Shroff, and P. Agarwal, “Long short term memory networks for anomaly detection in time series,” in ESANN 2015: 23rd Eur. Symp. on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, 2015 (2015), pp. 89–94.
V. Moshkin, N. Yarushkina, and I. Moshkina, “The intelligent system for interactive analysis and forecasting of graph data,” in Intelligent and Fuzzy Systems. INFUS 2022, Ed. by C. Kahraman, A. C. Tolga, S. Cevik Onar, S. Cebi, B. Oztaysi, and I. U. Sari, Lecture Notes in Networks and Systems, Vol. 504 (Springer, Cham, 2022), pp. 870–878. https://doi.org/10.1007/978-3-031-09173-5_100
T. Mikolov, “Recurrent neural network based language model,” (Brno Univ. of Technology, Brno, Czech Republic, 2010).
H. Sak, A. Senior, and F. Beaufays, “Long short-term memory recurrent neural network architectures for large scale acoustic modeling,” in Proc. Interspeech 2014 (ISCA, Mountain View, Calif., 2014), pp. 338–342. https://doi.org/10.21437/interspeech.2014-80
T. Wen and R. Keyes, “Time series anomaly detection using convolutional neural networks and transfer learning,” (2019). https://doi.org/10.48550/ar**v.1905.13628
N. Yarushkina, V. Moshkin, I. Andreev, V. Klein, and E. Beksaeva, “Hybridization of fuzzy inference and self-learning fuzzy ontology-based semantic data analysis,” in Proceedings of the First International Scientific Conference Intelligent Information Technologies for Industry (IITI’16), Ed. by A. Abraham, S. Kovalev, V. Tarassov, and V. Snášel, Advances in Intelligent Systems and Computing, Vol. 450 (Springer, Cham, 2016), pp. 277–285. https://doi.org/10.1007/978-3-319-33609-1_25
N. Yarushkina, I. Andreev, V. Moshkin, and I. Moshkina, “Integration of fuzzy OWL ontologies and fuzzy time series in the determination of faulty technical units,” in Computational Science and Its Applications-ICCSA 2019, Ed. by S. Misra, O. Gervasi, B. Murgante, E. Stankova, V. Kokhov, C. Torre, A. M. A. C. Rocha, D. Taniar, B. O. Apduhan, and E. Tarantino, Lecture Notes in Computer Science, Vol. 11619 (Springer, Cham, 2019), pp. 545–555. https://doi.org/10.1007/978-3-030-24289-3_40
C. Zhong, M. C. Gursoy, and S. Velipasalar, “Anomaly detection and sampling cost control via hierarchical GANs,” in GLOBECOM 2020-2020 IEEE Global Communications Conf., Taipei, 2020 (IEEE, 2020), pp. 1–6. https://doi.org/10.1109/globecom42002.2020.9322293
V. N. Zuev, “Detection of network traffic anomalies by deep learning method,” Program. Prod. Sist. 34 (1), 91–97 (2021). https://doi.org/10.15827/0236-235X.133.091-097
Funding
This work was supported by the Russian Science Foundation, project no. 23-71-01101 “Development of models and methods for improving the performance of data warehouses through predictive analysis of temporal diagnostic information.”
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
The authors declare that they have no conflicts of interest.
Additional information
Vadim Sergeevich Moshkin (born in 1990). He graduated from Ulyanovsk State Technical University in 2012, Candidate of Engineering Sciences (2017), Associate Professor of the Department of Information Systems of Ulyanovsk State Technical University. Director of the Department of Digital Transformation of Ulyanovsk State Technical University. Member of the Russian and European Associations of Artificial Intelligence. The list of scientific works includes more than 150 articles in the field of intellectual knowledge processing and design automation, as well as the construction of applied intelligent systems. Author ID (RSCI): 762084; Author ID (Scopus): 57190250573; Researcher ID (WoS): L-3578-2016.
Dmitriy Sergeevich Kurilo (born in 2000). Assistant of the Department of Information Systems of Ulyanovsk State Technical University. He has more than ten articles in the field. Author ID (RSCI): 10358; Author ID (Scopus): 57442659700; Researcher ID (WoS): GZN-0902-2022; ORCID: 0000-0003-0715-2210.
Ilya Alexeyevich Andreev (born in 1994). He graduated from Ulyanovsk State Technical University in 2017, graduate student in the Department of Information Systems of Ulyanovsk State Technical University. Head of the Laboratory of Automation of the Educational Process of Ulyanovsk State Technical University. He has more than 40 articles in the field of design automation, ontological engineering, and technical linguistics. Author ID (RSCI): 842148; Author ID (Scopus): 57190248754.
Rights and permissions
About this article
Cite this article
Moshkin, V.S., Kurilo, D.S. & Andreev, I.A. Hybridization of Ontologies and Neural Networks in the Problems of Detecting Anomalies of Time Series. Pattern Recognit. Image Anal. 33, 425–431 (2023). https://doi.org/10.1134/S105466182303032X
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1134/S105466182303032X