Time Series Anomaly Detection using Prediction-Reconstruction Mixture Errors by Wong, Lawrence C.

“…Anomaly detection on time series data is increasingly common across various industrial domains that require monitoring metrics to prevent potential accidents and economic losses. …”
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Applications of AI on Resource-ConstrainedHardware with a focus on Anomaly Detection by Ziegler, Travis

“…This thesis makes contributions to the field by (1) benchmarking common AI inference engines to identify trade-offs between them, (2) developing a framework that can assist neural-architecture searches to discover more efficient models, (3) proposing model conversion techniques that enable online-learning on MCUs, resulting in improved real-world accuracy, (4) creating a novel visual anomaly detector for MCUs, and (5) collecting a new dataset for anomaly detection benchmarks. The task of visual anomaly detection is to discern between known "Good" objects and objects that are slightly damaged or have imperfects. …”
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Modeling Control Signals for Reconstruction-based Time Series Anomaly Detection by Song, Grace Y.

“…Automated time series anomaly detection methods can provide insights while reducing the load placed on human experts in a variety of settings. …”
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Anomaly detection of 3D printing process using machine learning by Nur Muizzu Hamzah

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Combined anomaly detection framework for digital twins of water treatment facilities by Wei, Yuying, Law, Adrian Wing-Keung, Yang, Chun, Tang, Di

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Privacy-preserving anomaly detection in cloud manufacturing via federated transformer by Ma, Shiyao, Nie, Jiangtian, Kang, Jiawen, Lyu, Lingjuan, Liu, Ryan Wen, Zhao, Ruihui, Liu, Ziyao, Niyato, Dusit

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Unbiased multiple instance learning for weakly supervised video anomaly detection by Lv, Hui, Yue, Zhongqi, Sun, Qianru, Luo, Bin, Cui, Zhen, Zhang, Hanwang

Subjects: “…Weakly Supervised Video Anomaly Detection…”
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Applying graph neural network to multivariate time series anomaly detection by Mao, Yiyun

“…Motivated by the need to explore advanced anomaly detection techniques, this study investigates the performance of graph neural network-based anomaly detection models on multivariate time series data. …”
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Adaptive memory networks with self-supervised learning for unsupervised anomaly detection by Zhang, Yuxin, Wang, Jindong, Chen, Yiqiang, Yu, Han, Qin, Tao

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Analysis of machine learning application in campus network traffic anomaly detection by Li, Rongrong

“…The machine algorithms have higher accuracy than other algorithms in network data flow anomaly detection. This study has important reference value for campus network security research and verifies the important role of machine learning algorithms in detecting anomalies in campus network traffic.…”
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Anomaly detection through enhanced sentiment analysis on social media data by Zhao, Jingying

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Data-driven phase extraction for anomaly detection of repetitive human movements by Jatesiktat, Prayook

“…This standardization allows the principal component analysis reconstruction to be applied for anomaly detection. The whole modelling workflow not only works on a gold-standard marker-based motion capture system, but it is also compatible with our motion capture system which integrates a Kinect sensor with wrist-worn inertial measurement units (IMU). …”
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Anomaly detection in vessel tracking using Support Vector Machines (SVMs) by Dwi Handayani, Dini Oktarina, Sediono, Wahju, Shah, Asadullah

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Early warning systems for malaria outbreaks in Thailand: an anomaly detection approach by Srimokla, O, Pan-ngum, W, Khamsiriwatchara, A, Padungtod, C, Tipmontree, R, Choosri, N, Saralamba, S

“…In this study, an anomaly detection system is introduced as an early warning mechanism for potential malaria outbreaks in countries like Thailand.…”

Anomaly detection through spatio-temporal context modeling in crowded scenes by Lu, T., Wu, L., Ma, X., Shivakumara, P., Tan, C.L.

“…The proposed framework essentially turns the anomaly detection process into two parts, namely, motion pattern representation and crowded context modeling. …”
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Machine learning-based anomaly detection in NFV: a comprehensive survey by Sehar Zehra, Ummay Faseeha, Hassan Jamil Syed, Fahad Samad, Ashraf Osman Ibrahim Elsayed, Anas W. Abulfaraj, Wamda Nagmeldin

“…It proposes the utilization of anomaly detection techniques as a means to mitigate the potential risks of cyber attacks. …”
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Host-based packet header anomaly detection using statistical analysis by Yassin, Warusia, Udzir, Nur Izura, Abdullah, Azizol, Abdullah @ Selimun, Mohd Taufik, Muda, Zaiton, Zulzalil, Hazura

“…The exposure of network packets to frequent cyber attacks has increased the need for designing statistical-based anomaly detection recently. Conceptually, the statistical based anomaly detection attracts researcher's attention, but technically, the low attack detection rates remains an open challenges. …”
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Anomaly detection in the temperature of an AC motor using embedded machine learning by Ismail, Ezzeldin Ayman Ibrahim, Ahmad, Mohd. Ridzuan

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An anti virus scheme using digital signature and anomaly detection techniques by Subramaniam, Suresh Babu

“…The scheme comprises two layers of protection, where the first layer implements digital signature technique while the second layer implements anomaly detection technique. In the scheme, newly downloaded files that have been digitally signed using SHA-l and RSA algorithms are verified at the first layer. …”
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CICIDS-2017 dataset feature analysis with information gain for anomaly detection by Kurniabudi, Kurniabudi, Stiawan, Deris, Darmawijoyo, Darmawijoyo, Idris, Mohd. Yazid, Bamhdi, Alwi M., Budiarto, Rahmat

“…The objective of this study is to analyze relevant and significant features of huge network traffic to be used to improve the accuracy of traffic anomaly detection and to decrease its execution time. Information Gain is the most feature selection technique used in Intrusion Detection System (IDS) research. …”
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