Results 311 to 320 of about 5,378,381 (364)

TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data

Proceedings of the VLDB Endowment, 2022
Efficient anomaly detection and diagnosis in multivariate time-series data is of great importance for modern industrial applications. However, building a system that is able to quickly and accurately pinpoint anomalous observations is a challenging ...
Shreshth Tuli, G. Casale, N. Jennings
semanticscholar   +1 more source

AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection

International Conference on Learning Representations, 2023
Zero-shot anomaly detection (ZSAD) requires detection models trained using auxiliary data to detect anomalies without any training sample in a target dataset.
Qihang Zhou, Guansong Pang, Yu Tian, Shibo He, Jiming Chen   +4 more
semanticscholar   +1 more source

Revisiting Reverse Distillation for Anomaly Detection

Computer Vision and Pattern Recognition, 2023
Anomaly detection is an important application in large-scale industrial manufacturing. Recent methods for this task have demonstrated excellent accuracy but come with a latency trade-off.
Tran Dinh Tien, Anh Tuan Nguyen, Nguyen H. Tran, Ta Duc Huy, S. T. Duong, Chanh D. Tr, Nguyen, S. Q. Truong   +7 more
semanticscholar   +1 more source

MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection

Computer Vision and Pattern Recognition, 2019
The detection of anomalous structures in natural image data is of utmost importance for numerous tasks in the field of computer vision. The development of methods for unsupervised anomaly detection requires data on which to train and evaluate new ...
Paul Bergmann, Michael Fauser, David Sattlegger, C. Steger   +3 more
semanticscholar   +1 more source

Anomaly detection on the edge

MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM), 2017
Anomaly detection is the process of identifying unusual signals in a set of observations. This is a vital task in a variety of fields including cybersecurity and the battlefield. In many scenarios, observations are gathered from a set of distributed mobile or small form factor devices.
Joseph Schneible, Alex Lu
openaire   +1 more source

Conditional Anomaly Detection

IEEE Transactions on Knowledge and Data Engineering, 2007
When anomaly detection software is used as a data analysis tool, finding the hardest-to-detect anomalies is not the most critical task. Rather, it is often more important to make sure that those anomalies that are reported to the user are in fact interesting.
Xiuyao Song, Mingxi Wu, Christopher M. Jermaine, Sanjay Ranka   +3 more
openaire   +1 more source

Dataflow anomaly detection

2006 IEEE Symposium on Security and Privacy (S&P'06), 2006
Beginning with the work of Forrest et al, several researchers have developed intrusion detection techniques based on modeling program behaviors in terms of system calls. A weakness of these techniques is that they focus on control flows involving system calls, but not their arguments.
Sandeep Bhatkar, Abhishek Chaturvedi, R. Sekar 0001   +2 more
openaire   +1 more source

An Overview of Anomaly Detection

IT Professional, 2013
Security automation continues to depend on signature models, but vulnerability exploitation is exceeding the abilities of such models. The authors, in reviewing the different types of mathematical-based constructs in anomaly detection, reveal how anomaly detection can enhance network security by potentially solving problems that signature models can't ...
Char Sample, Kim Schaffer
openaire   +1 more source

PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization

ICPR Workshops, 2020
We present a new framework for Patch Distribution Modeling, PaDiM, to concurrently detect and localize anomalies in images in a one-class learning setting.
Thomas Defard, Aleksandr Setkov, Angélique Loesch, Romaric Audigier   +3 more
semanticscholar   +1 more source

Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network

Knowledge Discovery and Data Mining, 2019
Industry devices (i.e., entities) such as server machines, spacecrafts, engines, etc., are typically monitored with multivariate time series, whose anomaly detection is critical for an entity's service quality management.
Ya Su, Youjian Zhao, Chenhao Niu, Rong Liu, Wei Sun, Dan Pei   +5 more
semanticscholar   +1 more source