Advancing Autoencoder Architectures for Enhanced Anomaly Detection in Multivariate Industrial Time Series
Source abstract
Abstract
In the context of rapid digitization in industrial environments, how effective are advanced unsupervised learning models, particularly hybrid autoencoder models, at detecting anomalies in industrial control system (ICS) datas... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Anomaly Detection Techniques and Applications · Time Series Analysis and Forecasting
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (53)
Show source-provided references
1. A review of hybrid cyber threats modelling and detection using artificial intelligence in IIoT
2. The cybersecurity landscape in industrial control systems10.1109/JPROC.2015.2512235 ↗
3. Efficient and secure IoT based smart home automation using multi-model learning and blockchain technology
4. Security and privacy in smart farming: Challenges and opportunities10.1109/ACCESS.2020.2975142 ↗
5. A. Valdes and S. Cheung, “Intrusion monitoring in process control systems,” presented at the 42nd Hawaii Int. Conf. Syst. Sci., Waikoloa, HI, USA, Jan. 5–8, 2009, pp. 1–7.10.1109/HICSS.2009.273 ↗
6. B. Zhu, A. Joseph, and S. Sastry, “A taxonomy of cyber attacks on SCADA systems,” presented at the 2011 Int. Conf. Internet Things 4th Int. Conf. Cyber, Phys. Soc. Comput., Dalian, China, Oct. 19–22, 2011, pp. 380–388.10.1109/iThings/CPSCom.2011.34 ↗
7. Cross-dimension attentive feature fusion network for unsupervised time-series anomaly detection
8. D. S. Terzi, R. Terzi, and S. Sagiroglu, “Big data analytics for network anomaly detection from NetFlow data,” presented at the 2017 Int. Conf. Comput. Sci. Eng. (UBMK), Antalya, Turkey, Oct. 5–8, 2017, pp. 592–597.10.1109/UBMK.2017.8093473 ↗
9. R. Primartha and B. A. Tama, “Anomaly detection using random forest: A performance revisited,” presented at the 2017 Int. Conf. Data Softw. Eng. (ICoDSE), Palembang, Indonesia, Nov. 1–2, 2017, pp. 1–6.10.1109/ICODSE.2017.8285847 ↗
10. Tackling faults in the Industry 4.0 era—A survey of machine-learning solutions and key aspects10.3390/s20010109 ↗
11. A machine learning approach for anomaly detection in industrial control systems based on measurement data10.3390/electronics10040407 ↗
12. Development of a hybrid support vector machine with grey wolf optimization algorithm for detection of the solar power plants anomalies10.3390/systems11050237 ↗
13. Recursive principal component analysis-based data outlier detection and sensor data aggregation in IoT systems10.1109/JIOT.2017.2756025 ↗
14. On the generation of anomaly detection datasets in industrial control systems10.1109/ACCESS.2019.2958284 ↗
15. Y. Fu and F. Xue, “MAD: Self-supervised masked anomaly detection task for multivariate time series,” presented at the 2022 Int. Joint Conf. Neural Netw. (IJCNN), Padua, Italy, Jul. 18–23, 2022, pp. 1–8.10.1109/IJCNN55064.2022.9892218 ↗
16. C. Seong, Y. Song, J. Hyun, and Y. G. Cheong, “Towards building intrusion detection systems for multivariate time-series data,” presented at the Silicon Val. Cybersecurity Conf., San Jose, CA, USA, Dec. 2–3, 2021, pp. 45–56.10.1007/978-3-030-96057-5_4 ↗
17. Hybrid statistical-machine learning for real-time anomaly detection in industrial cyber-physical systems10.1109/TASE.2021.3073396 ↗
18. Unsupervised anomaly detection in flight data using convolutional variational auto-encoder10.3390/aerospace7080115 ↗
19. GRU-based interpretable multivariate time series anomaly detection in industrial control system10.1016/j.cose.2023.103094 ↗
20. Multivariate abnormal detection for industrial control systems using 1D CNN and GRU10.1109/ACCESS.2020.2993335 ↗
21. Y. Su et al., “Robust anomaly detection for multivariate time series through stochastic recurrent neural network,” presented at the 25th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., Anchorage, AK, USA, Aug. 4–8, 2019, pp. 2828–2837.10.1145/3292500.3330672 ↗
22. Anomaly-based intrusion detection from network flow features using variational autoencoder10.1109/ACCESS.2020.3001350 ↗
23. Adversarial sample attacks and defenses based on LSTM-ED in industrial control systems10.1016/j.cose.2024.103750 ↗
24. A traffic anomaly detection approach based on unsupervised learning for industrial cyber-physical system
25. J. Audibert, P. Michiardi, F. Guyard, S. Marti, and M. A. Zuluaga, “USAD: Unsupervised anomaly detection on multivariate time series,” presented at the 26th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., CA, USA, Jul. 6–10, 2020, pp. 3395–3404.10.1145/3394486.3403392 ↗
26. D. Li et al., “MAD-GAN: Multivariate anomaly detection for time series data with generative adversarial networks,” presented at the Int. Conf. Artif. Neural Netw., Munich, Germany, Sep. 17–19, 2019, pp. 703–716.10.1007/978-3-030-30490-4_56 ↗
27. TranAD: Deep transformer networks for anomaly detection in multivariate time series data10.14778/3514061.3514067 ↗
28. Unsupervised anomaly detection for IoT-based multivariate time series: Existing solutions, performance analysis and future directions10.3390/s23052844 ↗
29. Deep learning for anomaly detection: A review10.1145/3439950 ↗
30. H. K. Shin, W. Lee, S. Choi, J. H. Yun, and B. G. Min, “HAI (HIL-based augmented ICS) security dataset,” GitHub, 2023. Accessed: Mar. 1, 2024. [Online]. Available: https://github.com/icsdataset/hai
31. Unsupervised learning approach for anomaly detection in industrial control systems
32. A study on performance metrics for anomaly detection based on industrial control system operation data
33. ADT: Time series anomaly detection for cyber-physical systems via deep reinforcement learning10.1016/j.cose.2024.103825 ↗
34. A survey on anomaly detection for technical systems using LSTM networks
35. Variational LSTM enhanced anomaly detection for industrial big data10.1109/TII.2020.3022432 ↗
36. M Said Elsayed, N. A. Le-Khac, S. Dev, and A. D. Jurcut, “Network anomaly detection using LSTM based autoencoder,” presented at the 16th ACM Symp. QoS Sec. Wireless Mobile Netw, Alicante, Spain, Nov. 16–20, 2020, pp. 37–45.10.1145/3416013.3426457 ↗
37. Framewise phoneme classification with bidirectional LSTM and other neural network architectures10.1016/j.neunet.2005.06.042 ↗
38. W. Tang, G. Long, L. Liu, T. Zhou, M. Blumenstein and J. Jiang, “Omni-scale CNNs: A simple and effective kernel size configuration for time series classification,” presented at the Int. Conf. Learn. Rep. (ICLR), Virtual, Apr. 25–29, 2022, pp. 1–17.
39. A one-dimensional convolutional neural network (1D-CNN) based deep learning system for network intrusion detection10.3390/app12167986 ↗
40. Bearing fault diagnosis based on vibro-acoustic data fusion and 1D-CNN network10.1016/j.measurement.2020.108518 ↗
41. C. Zhou and R. C. Paffenroth, “Anomaly detection with robust deep autoencoders,” presented at the 23rd ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. (KDD), Halifax, NS, Canada, Aug. 13–17, 2017, pp. 655–674.10.1145/3097983.3098052 ↗
42. Improved autoencoder for unsupervised anomaly detection10.1002/int.22582 ↗
43. A bidirectional LSTM deep learning approach for intrusion detection10.1016/j.eswa.2021.115524 ↗
44. A. Farzad and T. A. Gulliver, “Log message anomaly detection and classification using auto-B/LSTM and auto-GRU,” Nov. 2019. doi: 10.48550/arXiv.1911.08744.10.31224/osf.io/d4e6a ↗
45. Z. Qu, L. Su, X. Wang, S. Zheng, X. Song and X. Song, “An unsupervised learning method of anomaly detection using GRU,” presented at the 2018 IEEE Int. Conf. Big Data Smart Comput. (BigComp), Shanghai, China, Jan. 15–17, 2018, pp. 685–688.10.1109/BigComp.2018.00126 ↗
46. A ConvBiLSTM deep learning model-based approach for Twitter sentiment classification10.1109/ACCESS.2021.3064830 ↗
47. SSS-AE: Anomaly detection using self-attention based sequence-to-sequence auto-encoder in SMD assembly machine sound10.1109/ACCESS.2021.3112545 ↗
48. Fault detection for motor drive control system of industrial robots using CNN-LSTM-based observers10.30941/CESTEMS.2023.00014 ↗
49. Anomaly detection dataset for industrial control systems10.1109/ACCESS.2023.3320928 ↗
50. Deep learning-based intrusion detection approach for securing industrial Internet of Things10.1016/j.aej.2023.09.023 ↗
51. An explainable multi-modal model for advanced cyber-attack detection in industrial control systems10.1016/j.iot.2024.101092 ↗
52. A hybrid methodology for anomaly detection in cyber-physical systems10.1016/j.neucom.2023.127068 ↗
53. A feature selection based on genetic algorithm for intrusion detection of industrial control systems10.1016/j.cose.2023.103675 ↗