Supervised Anomaly Detection in Uncertain Pseudoperiodic Data Streams
Uncertain data streams have been widely generated in many Web applications. The uncertainty in data streams makes anomaly detection from sensor data streams far more challenging. In this paper, we present a novel framework that supports anomaly detection in uncertain data streams. The proposed framework adopts an efficient uncertainty pre-processing procedure to identify and eliminate uncertainties in data streams. Based on the corrected data streams, we develop effective period pattern recognition and feature extraction techniques to improve the computational efficiency. We use classification methods for anomaly detection in the corrected data stream. We also empirically show that the proposed approach shows a high accuracy of anomaly detection on a number of real datasets.
Code (0)
등록된 구현이 없습니다.
Tasks
Anomaly DetectionComputational EfficiencyGeneral ClassificationSupervised Anomaly DetectionSimilar Papers 제목 키워드 기반
U2AD: Uncertainty-based Unsupervised Anomaly Detection Framework for Detecting T2 Hyperintensity in MRI Spinal Cord
T2 hyperintensities in spinal cord MR images are crucial biomarkers for conditions such as degenerative cervical myelopathy. However, current clinical diagnoses primarily rely on manual evaluation. Deep learning methods …
Anomaly DetectionLesion DetectionUnsupervised Anomaly DetectionUncertainty-aware Human Mobility Modeling and Anomaly Detection
Given the temporal GPS coordinates from a large set of human agents, how can we model their mobility behavior toward effective anomaly (e.g. bad-actor or malicious behavior) detection without any labeled data? Human mobi…
Anomaly DetectionDecision MakingTrajectory ModelingA Synergy Scoring Filter for Unsupervised Anomaly Detection with Noisy Data
Noise-inclusive fully unsupervised anomaly detection (FUAD) holds significant practical relevance. Although various methods exist to address this problem, they are limited in both performance and scalability. Our work se…
Anomaly DetectionUnsupervised Anomaly DetectionBayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection
Despite numerous studies of deep autoencoders (AEs) for unsupervised anomaly detection, AEs still lack a way to express uncertainty in their predictions, crucial for ensuring safe and trustworthy machine learning systems…
Anomaly DetectionUncertainty QuantificationUnsupervised Anomaly DetectionInterpreting Rate-Distortion of Variational Autoencoder and Using Model Uncertainty for Anomaly Detection
Building a scalable machine learning system for unsupervised anomaly detection via representation learning is highly desirable. One of the prevalent methods is using a reconstruction error from variational autoencoder (V…
Anomaly DetectionBIG-bench Machine LearningRepresentation LearningUnsupervised Anomaly Detection