Insights into Anomaly Detection: A Survey and Comparative Analysis of Techniques for Time Series Data from Industrial Environment

DOI

10.2991/978-94-6463-662-8_71How to use a DOI?

Keywords

anomaly detection; temporal data analysis; machine learning models; network surveillance; cybersecurity insights

Abstract

This research explores a spectrum of anomaly detection strategies for time-series datasets, emphasizing statistical frameworks, advanced machine learning algorithms, and integrated hybrid techniques. Approaches such as Improved Influenced Outlierness (IIO) and variants of Support Vector Machines (SVM) in unsupervised and semi-supervised modes are analyzed for their ability to detect outlier points and address intricate data patterns. Deep learning models, including Enhanced Autoencoders (EAE), Multi-Layer Convolutional Networks (ML-CN), and Advanced Deep Autoencoders (ADA), are evaluated for their capability to uncover anomalies in high-dimensional, temporally correlated data. Moreover, methods like Resilient Principal Component Analysis (RPCA), Autoregressive Integrated Moving Average (ARIMA), and disk-optimized algorithms are discussed for domain-specific tasks, including network anomaly tracking, breach detection, and congestion management in sensor networks. By critically assessing the benefits, drawbacks, and operational scenarios of these techniques, this study offers a detailed reference for selecting optimal models, facilitating precise anomaly detection across varied industrial and technical application.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - K. Arun Prasad
AU  - G. Pattabirani
AU  - K. Sundaramoorthy
PY  - 2025
DA  - 2025/03/17
TI  - Insights into Anomaly Detection: A Survey and Comparative Analysis of Techniques for Time Series Data from Industrial Environment
BT  - Proceedings of the International Conference on Advanced Materials, Manufacturing and Sustainable Development (ICAMMSD 2024)
PB  - Atlantis Press
SP  - 871
EP  - 907
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-662-8_71
DO  - 10.2991/978-94-6463-662-8_71
ID  - Prasad2025
ER  -