#Anomaly Detection
Latest news articles tagged with "Anomaly Detection". Follow the timeline of events, related topics, and entities.
Articles (16)
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🇺🇸 A Novel Solution for Zero-Day Attack Detection in IDS using Self-Attention and Jensen-Shannon Divergence in WGAN-GP
[USA]
arXiv:2603.19350v1 Announce Type: cross Abstract: The increasing sophistication of cyber threats, especially zero-day attacks, poses a significant challenge to cybersecurity. Zero-day attacks exploit...
Related: #Cybersecurity, #Machine Learning -
🇺🇸 FB-CLIP: Fine-Grained Zero-Shot Anomaly Detection with Foreground-Background Disentanglement
[USA]
arXiv:2603.19608v1 Announce Type: cross Abstract: Fine-grained anomaly detection is crucial in industrial and medical applications, but labeled anomalies are often scarce, making zero-shot detection ...
Related: #Computer Vision -
🇺🇸 CORE: Robust Out-of-Distribution Detection via Confidence and Orthogonal Residual Scoring
[USA]
arXiv:2603.18290v1 Announce Type: new Abstract: Out-of-distribution (OOD) detection is essential for deploying deep learning models reliably, yet no single method performs consistently across archite...
Related: #Machine Learning -
🇺🇸 RangeAD: Fast On-Model Anomaly Detection
[USA]
arXiv:2603.17795v1 Announce Type: cross Abstract: In practice, machine learning methods commonly require anomaly detection (AD) to filter inputs or detect distributional shifts. Typically, this is im...
Related: #Machine Learning -
🇺🇸 AdapTS: Lightweight Teacher-Student Approach for Multi-Class and Continual Visual Anomaly Detection
[USA]
arXiv:2603.17530v1 Announce Type: cross Abstract: Visual Anomaly Detection (VAD) is crucial for industrial inspection, yet most existing methods are limited to single-category scenarios, failing to a...
Related: #Machine Learning -
🇺🇸 Unsupervised Symbolic Anomaly Detection
[USA]
arXiv:2603.17575v1 Announce Type: cross Abstract: We propose SYRAN, an unsupervised anomaly detection method based on symbolic regression. Instead of encoding normal patterns in an opaque, high-dimen...
Related: #Machine Learning -
🇺🇸 Geometry-Aware Semantic Reasoning for Training Free Video Anomaly Detection
[USA]
arXiv:2603.13374v1 Announce Type: cross Abstract: Training-free video anomaly detection (VAD) has recently emerged as a scalable alternative to supervised approaches, yet existing methods largely rel...
Related: #Computer Vision -
🇺🇸 Hierarchical Reference Sets for Robust Unsupervised Detection of Scattered and Clustered Outliers
[USA]
arXiv:2603.12847v1 Announce Type: cross Abstract: Most real-world IoT data analysis tasks, such as clustering and anomaly event detection, are unsupervised and highly susceptible to the presence of o...
Related: #Machine Learning -
🇺🇸 Surprised by Attention: Predictable Query Dynamics for Time Series Anomaly Detection
[USA]
arXiv:2603.12916v1 Announce Type: cross Abstract: Multivariate time series anomalies often manifest as shifts in cross-channel dependencies rather than simple amplitude excursions. In autonomous driv...
Related: #Time Series Analysis -
🇺🇸 Anomaly detection in time-series via inductive biases in the latent space of conditional normalizing flows
[USA]
arXiv:2603.11756v1 Announce Type: new Abstract: Deep generative models for anomaly detection in multivariate time-series are typically trained by maximizing data likelihood. However, likelihood in ob...
Related: #Machine Learning -
🇺🇸 Temporal-Conditioned Normalizing Flows for Multivariate Time Series Anomaly Detection
[USA]
arXiv:2603.09490v1 Announce Type: cross Abstract: This paper introduces temporal-conditioned normalizing flows (tcNF), a novel framework that addresses anomaly detection in time series data with accu...
Related: #Time Series Analysis -
🇺🇸 GNNs for Time Series Anomaly Detection: An Open-Source Framework and a Critical Evaluation
[USA]
arXiv:2603.09675v1 Announce Type: cross Abstract: There is growing interest in applying graph-based methods to Time Series Anomaly Detection (TSAD), particularly Graph Neural Networks (GNNs), as they...
Related: #Machine Learning, #Open Source -
🇺🇸 The Boiling Frog Threshold: Criticality and Blindness in World Model-Based Anomaly Detection Under Gradual Drift
[USA]
arXiv:2603.08455v1 Announce Type: new Abstract: When an RL agent's observations are gradually corrupted, at what drift rate does it "wake up" -- and what determines this boundary? We study world mode...
Related: #System Failure -
🇺🇸 AegisUI: Behavioral Anomaly Detection for Structured User Interface Protocols in AI Agent Systems
[USA]
arXiv:2603.05031v1 Announce Type: new Abstract: AI agents that build user interfaces on the fly assembling buttons, forms, and data displays from structured protocol payloads are becoming common in p...
Related: #AI Security, #User Interface Protocols -
🇺🇸 Drift-Aware Variational Autoencoder-based Anomaly Detection with Two-level Ensembling
[USA]
arXiv:2602.12976v1 Announce Type: cross Abstract: In today's digital world, the generation of vast amounts of streaming data in various domains has become ubiquitous. However, many of these data are ...
Related: #Machine Learning, #Data Science -
🇺🇸 Power Interpretable Causal ODE Networks: A Unified Model for Explainable Anomaly Detection and Root Cause Analysis in Power Systems
[USA]
arXiv:2602.12592v1 Announce Type: cross Abstract: Anomaly detection and root cause analysis (RCA) are critical for ensuring the safety and resilience of cyber-physical systems such as power grids. Ho...
Related: #Machine Learning, #Power Systems, #Explainable AI
Key Entities (1)
- Explainable artificial intelligence (1 news)
About the topic: Anomaly Detection
The topic "Anomaly Detection" aggregates 16+ news articles from various countries.