Results 321 to 330 of about 5,378,381 (364)

A Survey of Deep Anomaly Detection in Multivariate Time Series: Taxonomy, Applications, and Directions

Italian National Conference on Sensors
Multivariate time series anomaly detection (MTSAD) can effectively identify and analyze anomalous behavior in complex systems, which is particularly important in fields such as financial monitoring, industrial equipment fault detection, and cybersecurity.
Fengling Wang, Yiyue Jiang, Rongjie Zhang, Aimin Wei, Jingming Xie, Xiongwen Pang   +5 more
semanticscholar   +1 more source

Iterative anomaly detection

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017
Anomaly detection (AD) is designed to find targets that are spectrally distinct from their surrounding neighborhood. Unfortunately, commonly used anomaly detectors generally do not take into account its surrounding spatial information. This paper derives an iterative version of anomaly detection, iterative anomaly detection (IAD) to address this issue.
Yulei Wang 0002, Bai Xue, Lin Wang 0028, Hsiao-Chi Li, Li-Chien Lee, Chunyan Yu, Meiping Song, Sen Li, Chein-I Chang   +8 more
openaire   +1 more source

AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection

European Conference on Computer Vision
Zero-shot anomaly detection (ZSAD) targets the identification of anomalies within images from arbitrary novel categories. This study introduces AdaCLIP for the ZSAD task, leveraging a pre-trained vision-language model (VLM), CLIP.
Yunkang Cao, Jiangning Zhang, Luca Frittoli, Yuqi Cheng, Weiming Shen, Giacomo Boracchi   +5 more
semanticscholar   +1 more source

Anomaly detection in substation networks

Journal of Information Security and Applications, 2020
Fundamental components of the distribution systems of electric energy are primary and secondary substation networks. Considering the incorporation of legacy communication infrastructure in these systems, they often have inherent cybersecurity vulnerabilities.
Kreimel P., Eigner O., Mercaldo F., Santone A., Tavolato P.   +4 more
openaire   +2 more sources

Anomaly detection in trajectories

2016 24th Signal Processing and Communication Application Conference (SIU), 2016
In this work, we study the problem of anomaly detection of the trajectories of objects in a visual scene. For this purpose, we propose a novel representation for trajectories utilizing covariance features. Representing trajectories via co-variance features enables us to calculate the distance between the trajectories of different lengths. After setting
Hamza Ergezer, Kemal Leblebicioglu 0001
openaire   +1 more source

Sparse Coding with Anomaly Detection

Journal of Signal Processing Systems, 2013
We consider the problem of simultaneous sparse coding and anomaly detection in a collection of data vectors. The majority of the data vectors are assumed to conform with a sparse representation model, whereas the anomaly is caused by an unknown subset of the data vectors - the outliers - which significantly deviate from this model.
Amir Adler, Michael Elad, Yacov Hel-Or, Ehud Rivlin   +3 more
openaire   +1 more source

Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery

Information Processing in Medical Imaging, 2017
Obtaining models that capture imaging markers relevant for disease progression and treatment monitoring is challenging. Models are typically based on large amounts of data with annotated examples of known markers aiming at automating detection.
T. Schlegl, Philipp Seeböck, S. Waldstein, U. Schmidt-Erfurth, G. Langs   +4 more
semanticscholar   +1 more source

Anomaly detection for diagnosis

[1990] Digest of Papers. Fault-Tolerant Computing: 20th International Symposium, 2002
The author presents a method for detecting anomalous events in communication networks and other similarly characterized environments in which performance anomalies are indicative of failure. The methodology, based on automatically learning the difference between normal and abnormal behavior, has been implemented as part of an automated diagnosis system
openaire   +1 more source

MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection

Neural Information Processing Systems
Recent advancements in anomaly detection have seen the efficacy of CNN- and transformer-based approaches. However, CNNs struggle with long-range dependencies, while transformers are burdened by quadratic computational complexity. Mamba-based models, with
Haoyang He, Yuhu Bai, Jiangning Zhang, Qingdong He, Hongxu Chen, Zhenye Gan, Chengjie Wang, Xiangtai Li, Guanzhong Tian, Lei Xie   +9 more
semanticscholar   +1 more source

Detecting Anomalies in Graphs

2007 IEEE Intelligence and Security Informatics, 2007
Graph data represents relationships, connections, or affinities. Normal relationships produce repeated, and so common, substructures in graph data. We present techniques for discovering anomalous substructures in graphs, for example small cliques, nodes with unusual neighborhoods, or small unusual subgraphs, using extensions of spectral graph ...
openaire   +1 more source