A Comparative Evaluation of Unsupervised Anomaly Detection …
Apr 19, 2016 · Anomaly detection is the process of identifying unexpected items or events in datasets, which differ from the norm. In contrast to standard classification tasks, anomaly …
This challenge is known as unsupervised anomaly detection and is addressed in many practical applications, for example in network intrusion detection, fraud detection as well as in the life …
A simple method for unsupervised anomaly detection: An ... - PLOS
Jan 11, 2022 · We propose a simple anomaly detection method that is applicable to unlabeled time series data and is sufficiently tractable, even for non-technical entities, by using the …
Anomaly detection in virtual machine logs against irrelevant …
Jan 7, 2025 · Specifically, this study aims to enhance the effectiveness of anomaly detection in virtual machine logs, particularly in the context of irrelevant attribute interference. By …
Generative adversarial local density-based unsupervised anomaly …
Jan 24, 2025 · In response to the difficulty of existing methods to learn the distribution patterns of the data to be detected in an unsupervised scenario, leading to low accuracy in detecting …
Detection of overdose and underdose prescriptions—An …
Nov 19, 2021 · In the present study, we used the one-class support vector machine (OCSVM), one of the most common unsupervised machine learning algorithms for anomaly detection, to …
An improved X-means and isolation forest based methodology for …
Jan 31, 2022 · We compared X-iForest with seven mainstream unsupervised algorithms in terms of the AUC and anomaly detection rates. A large number of experiments showed that X …
Anomaly detection in multivariate time series data using deep
Jun 6, 2024 · The investigation [25] looks at unsupervised anomaly detection (AD) algorithms for multivariate time series (MTS) from the Internet of Things (I). It lays the theoretical …
An anomaly detection scheme for data stream in cold chain logistics
Mar 10, 2025 · In this paper, a novel CCL anomaly detection scheme is proposed, which not only considers the characteristics of data flow of the collected data of CCL, but also …
An incremental anomaly detection model for virtual machines
Nov 8, 2017 · Self-Organizing Map (SOM) algorithm as an unsupervised learning method has been applied in anomaly detection due to its capabilities of self-organizing and automatic …