A Review of Anomaly Detection Machine Learning and Deep Learning Techniques
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Abstract
In general, an anomaly may be defined as a variance or departure from what is expected or normal. According to historical records, the term "anomaly" was derived from the Greek word "anomalia," which translates as "uneven" or "irregular." Many examples of such abnormalities or deviations from normalcy have occurred in our everyday lives, and we have all seen them. For example, when a condition monitoring system detects any value or parameter of the machine that falls outside the minimum value limit to the maximum value limit, it beeps an alert. Similarly, when credit card fraud is detected, it notifies both the bank and the customer immediately. Currently, the most challenging problem is determining how to identify irregularities in data streams. An example of a data stream is a continuous stream of data that is continuously formed from any source and is referred to as streaming data (also known as streaming information). Finding anomalies in such a significant amount of data will be a time-consuming and challenging endeavour. In this paper, we will delve into the details of data streams and anomaly detection in data streams, examining a substantial number of papers and articles published on the topic in recent years.
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D. Deng, "Research on Anomaly Detection Method Based on DBSCAN Clustering Algorithm," 2020 5th International Conference on Information Science, Computer Technology and Transportation (ISCTT), 2020, pp. 439-442, DOI: https://doi.org/10.1109/ISCTT51595.2020.00083
M. Injadat, F. Salo, A. B. Nassif, A. Essex, and A. Shami, ‘‘Bayesian optimization with machine learning algorithms towards anomaly detection,’’ in Proc. IEEE Global Commun. Conf. (GLOBECOM), Dec. 2018, pp. 1–6. DOI: https://doi.org/10.1109/GLOCOM.2018.8647714
T. Schlegl, P. Seeböck, S. M. Waldstein, U. Schmidt-Erfurth, and G. Langs, Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery, vol. 10265, no. 2. Cham, Switzerland: Springer, 2017. DOI: https://doi.org/10.48550/arXiv.1703.05921
F. Salo, M. Injadat, A. B. Nassif, A. Shami, and A. Essex, ‘‘Data mining techniques in intrusion detection systems: A systematic literature review,’’ IEEE Access, vol. 6, pp. 56046–56058, 2018. https://ieeexplore.ieee.org/document/8476553/
F. Salo, M. N. Injadat, A. Moubayed, A. B. Nassif, and A. Essex, ‘‘Clustering enabled classification using ensemble feature selection for intrusion detection,’’ in Proc. Int. Conf. Comput., Netw. Commun. (ICNC), 2019, pp. 276–281. https://nchr.elsevierpure.com/en/publications/clustering-enabled-classification-using-ensemble-feature-selectio
K. Agrawal, T. Alladi, A. Agrawal, V. Chamola and A. Benslimane, "NovelADS: A Novel Anomaly Detection System for Intra-Vehicular Networks," in IEEE Transactions on Intelligent Transportation Systems, DOI: https://doi.org/10.1109/TITS.2022.3146024
T. Shin, H. Sun, J. Zhu and Y. Zhang, "Nondestructive damage detection of concrete with alkali-silica reactions using coda wave and anomaly detection," in IEEE Sensors Journal, DOI: https://doi.org/10.1109/JSEN.2022.3149721
M. Odiathevar, W. K. G. Seah and M. Frean, "A Bayesian Approach To Distributed Anomaly Detection In Edge AI Networks," in IEEE Transactions on Parallel and Distributed Systems, DOI: https://doi.org/10.1109/TPDS.2022.3151853
F. Pérez-Bueno, L. García, G. Maciá-Fernández and R. Molina, "Leveraging a Probabilistic PCA Model to Understand the Multivariate Statistical Network Monitoring Framework for Network Security Anomaly Detection," in IEEE/ACM Transactions on Networking, DOI: https://doi.org/10.1109/TNET.2021.3138536
L. Deng, D. Lian, Z. Huang and E. Chen, "Graph Convolutional Adversarial Networks for Spatiotemporal Anomaly Detection," in IEEE Transactions on Neural Networks and Learning Systems, DOI: https://doi.org/10.1109/TNNLS.2021.3136171
L. Xi, R. Wang and Z. J. Haas, "Data-correlation-aware Unsupervised Deep-Learning Model for Anomaly Detection in Cyber-Physical Systems," in IEEE Internet of Things Journal, DOI: https://doi.org/10.1109/JIOT.2022.3150048
S. Wang, X. Wang, L. Zhang and Y. Zhong, "Auto-AD: Autonomous Hyperspectral Anomaly Detection Network Based on Fully Convolutional Autoencoder," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-14, 2022, Art. no. 5503314,
DOI: https://doi.org/10.1109/TGRS.2021.3057721
K. M. León-López, F. Mouret, H. Arguello and J. -Y. Tourneret, "Anomaly Detection and Classification in Multispectral Time Series Based on Hidden Markov Models," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-11, 2022, Art. no. 5402311, DOI: https://doi.org/10.1109/TGRS.2021.3101127
M. Babaei and M. Imani, "Anomaly Detection Improvement Using Sparse Representation and Morphological Profile," 2020 6th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS), 2020, pp. 1-5, DOI: https://doi.org/10.1109/ICSPIS51611.2020.9349597
C. Huang et al., "Self-Supervision-Augmented Deep Autoencoder for Unsupervised Visual Anomaly Detection," in IEEE Transactions on Cybernetics, DOI: https://doi.org/10.1109/TCYB.2021.3127716
C. Wang, Y. Yao and H. Yao, "Video anomaly detection method based on future frame prediction and attention mechanism," 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), 2021, pp. 0405-0407,