CONCEPTUAL FOUNDATIONS OF ANOMALY DETECTION IN INDUSTRIAL INFORMATION SYSTEMS USING MACHINE LEARNING
DOI:
https://doi.org/10.34185/1991-7848.itmm.2026.01.072Keywords:
machine learning, anomaly detection, cybersecurity, autoencoder, industrial systems, behavioral analysisAbstract
The paper provides a conceptual overview of anomaly detection in industrial information systems with a focus on machine learning methods. The discussion emphasizes unsupervised approaches for modeling normal system behavior and detecting deviations without predefined attack patterns. The work is descriptive and aims to clarify the underlying principles rather than present experimental validation.
References
Goodfellow I., Bengio Y., Courville A. Deep Learning. Cambridge: MIT Press, 2016. URL: https://www.deeplearningbook.org
Chandola V., Banerjee A., Kumar V. Anomaly Detection: A Survey. ACM Computing Surveys. 2009. Vol. 41, No. 3. P. 1–58. DOI: 10.1145/1541880.1541882
Hinton G. E., Salakhutdinov R. R. Reducing the dimensionality of data with neural networks. Science. 2006. Vol. 313. P. 504–507. DOI: 10.1126/science.1127647
Aggarwal C. C. Outlier Analysis. 2nd ed. Springer, 2017. DOI: 10.1007/978-3-319-47578-3
Ahmed M., Mahmood A. N., Hu J. A survey of network anomaly detection techniques. Journal of Network and Computer Applications. 2016. Vol. 60. P. 19–31. DOI: 10.1016/j.jnca.2015.11.016
Goh J., Adepu S., Junejo K. N., Mathur A. A dataset to support research in the design of secure water treatment systems (SWaT). International Conference on Critical Information Infrastructures Security. 2016. DOI: 10.1007/978-3-319-71368-7_25
