Results 61 to 70 of about 109,015 (219)
A dubiety-determining based model for database cumulated anomaly intrusion
, 2007 The concept of Cumulated Anomaly (CA), which describes a new type of database anomalies, is addressed. A typical CA intrusion is that when a user who is authorized to modify data records under certain constraints deliberately hides his/her intentions ...
Yi, J +5 more
core +1 more source
Intelligent System Design for Advanced Anomaly Detection in Structured Datasets
, 2023 reservedAnomaly detection in structured datasets plays a pivotal role across a wide array of application domains, often serving as a critical tool for identifying significant, actionable insights.
ZANATTA, MICHELE
core
Anomaly detection by robust statistics [PDF]
WIREs Data Mining and Knowledge Discovery, 2017 Real data often contain anomalous cases, also known as outliers. These may spoil the resulting analysis but they may also contain valuable information. In either case, the ability to detect such anomalies is essential. A useful tool for this purpose is robust statistics, which aims to detect the outliers by first fitting the majority of the data and ...
Rousseeuw, Peter, Hubert, Mia
openaire +3 more sources
Anomaly Detection Using Graph Anomaly Rules
Big Data Mining and AnalyticsAnomaly detection in attribute networks is utilized to discover patterns of individuals or groups that deviate from the majority, and is widely used in areas such as e-commerce and social media.
Bowen Dong +4 more
doaj +1 more source
Comparative Analysis of Anomaly Detection Techniques Using Generative Adversarial Network
Sir Syed University Research Journal of Engineering and Technology, 2023 Anomaly detection in a piece of data is a challenging task. Researchers use different approaches to classify data as anomalous. These include traditional, supervised, unsupervised, and semi-supervised techniques.
Imran Ullah Khan +4 more
doaj
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 Anomaly detection has become one of the crucial tasks in hyperspectral images processing. However, most deep learning-based anomaly detection methods often suffer from the incapability of utilizing spatial–spectral information, which decreases the
Song Xiao +5 more
doaj +1 more source
Anomaly Detection: theoretical analysis and investigation on the Isolation Forest method
, 2023 openQuesto lavoro di tesi si concentra sull’analisi teorica e l’implementazione pratica dell’ Anomaly detection, intesa come rilevamento di anomalie, tramite metodi di machine learning, per identificare eventi che risultano sospetti in relazione al ...
BRESSAN, ALBERTO
core
Hyperspectral Anomaly Detection Method Based on Low Rank Total Variation Regu-larization
Jisuanji kexue yu tansuo, 2020 Hyperspectral remote sensing technology provides abundant spectral information for exploring objects and supplies a better data source for anomaly detection.
XU Chao, ZHAN Tianming
doaj +1 more source
Surface anomaly detection on island-based PV panels using edge neural networks
Zhejiang dianliSurface anomaly detection on photovoltaic （PV） panels is crucial for their operation and maintenance， especially in island environments where challenges such as small anomaly sizes and minimal color differences are prevalent. Due to the poor accuracy and
ZHANG Yinxian, ZHANG Zhanyao, ZHANG Xiya
doaj +1 more source
UniFlow: Unified Normalizing Flow for Unsupervised Multi-Class Anomaly Detection
InformationMulti-class anomaly detection is more efficient and less resource-consuming in industrial anomaly detection scenes that involve multiple categories or exhibit large intra-class diversity.
Jianmei Zhong, Yanzhi Song
doaj +1 more source