Anomaly Detection
Anomaly detection focuses on identifying unusual patterns or deviations from expected behavior within data, aiming to improve system reliability and safety across diverse applications. Current research emphasizes unsupervised and self-supervised learning approaches, employing architectures like autoencoders, transformers, and graph neural networks, often incorporating techniques such as Bayesian inference and metric learning to enhance robustness and interpretability. The field's significance stems from its broad applicability, ranging from fraud detection and medical diagnosis to industrial process monitoring and network security, with ongoing efforts to develop more efficient, accurate, and explainable methods.
Papers
Label Assisted Autoencoder for Anomaly Detection in Power Generation Plants
Marcellin Atemkeng, Victor Osanyindoro, Rockefeller Rockefeller, Sisipho Hamlomo, Jecinta Mulongo, Theophilus Ansah-Narh, Franklin Tchakounte, Arnaud Nguembang Fadja
Perception Datasets for Anomaly Detection in Autonomous Driving: A Survey
Daniel Bogdoll, Svenja Uhlemeyer, Kamil Kowol, J. Marius Zöllner
Leveraging a Probabilistic PCA Model to Understand the Multivariate Statistical Network Monitoring Framework for Network Security Anomaly Detection
Fernando Pérez-Bueno, Luz García, Gabriel Maciá-Fernández, Rafael Molina
Eloss in the way: A Sensitive Input Quality Metrics for Intelligent Driving
Haobo Yang, Shiyan Zhang, Zhuoyi Yang, Xinyu Zhang
Streaming Anomaly Detection
Siddharth Bhatia
BSSAD: Towards A Novel Bayesian State-Space Approach for Anomaly Detection in Multivariate Time Series
Usman Anjum, Samuel Lin, Justin Zhan
FractalAD: A simple industrial anomaly detection method using fractal anomaly generation and backbone knowledge distillation
Xuan Xia, Weijie Lv, Xing He, Nan Li, Chuanqi Liu, Ning Ding