arXiv AI
Jul 24
CLOE: Christoffel Loss Autoencoder for Anomaly Detection
arXiv:2607. 20530v1 Announce Type: cross Abstract: Semi-supervised anomaly detection plays a key role in diverse fields such as process monitoring, healthcare, and finance.
D2H-AD: A Hybrid Model Utilizing Hyperdimensional Computing for Advanced Anomaly Detection
arXiv:2606. 13754v1 Announce Type: new Abstract: Anomaly detection is a fundamental component of intelligent systems with applications in healthcare, cybersecurity, smart grids, and IoT environments.
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arXiv AI
Aug 10
FedVAR: Prototype-Aligned Federated Framework for Video Anomaly Recognition
arXiv:2608. 06876v1 Announce Type: cross Abstract: In the era of Industrial Internet of Things (IIoT) and Cyber-Physical Systems (CPS), Federated Learning (FL) offers a promising decentralized intelligence paradigm for Video Anomaly Recognition (VAR).
arXiv AI
Jun 11
OCSVM-Guided Representation Learning for Unsupervised Anomaly Detection
arXiv:2507. 21164v2 Announce Type: replace-cross Abstract: Unsupervised anomaly detection (UAD) aims to detect anomalies without labeled data, a necessity in many machine learning applications where anomalous samples are rare or not available.
arXiv AI
Jul 17
VAN-AD: Visual Masked Autoencoder with Normalizing Flow For Time Series Anomaly Detection
arXiv:2603. 26842v3 Announce Type: replace-cross Abstract: Time series anomaly detection (TSAD) is essential for maintaining the reliability and security of IoT-enabled service systems.
arXiv Machine Learning
Jul 31
ARES: Anomaly Recognition Model For Edge Streams
arXiv:2511. 22078v2 Announce Type: replace Abstract: Many real-world scenarios involving streaming information can be represented as temporal graphs, where data flows through dynamic changes in edges over time.
arXiv Machine Learning
Jun 19
We Need to Rethink Benchmarking in Anomaly Detection
arXiv:2507. 15584v2 Announce Type: replace Abstract: Despite the continuous proposal of new anomaly detection algorithms and extensive benchmarking efforts, progress seems to stagnate, with only minor performance differences between established baselines and new algorithms.
arXiv AI
Jun 26
Digital Twin-Driven Communication-Efficient Federated Anomaly Detection for Industrial IoT
arXiv:2601. 01701v2 Announce Type: replace-cross Abstract: Anomaly detection is increasingly becoming crucial for maintaining the safety, reliability, and efficiency of industrial systems.
arXiv Machine Learning
Jul 15
Lightweight Multi-Scale Anomaly Detection for Resource-Constrained Edge Devices
arXiv:2607. 12599v1 Announce Type: new Abstract: Time-series anomaly detection is increasingly important in IoT systems, sensor networks, and edge monitoring applications, where models must operate under strict constraints on memory, latency, and power consumption.
arXiv Machine Learning
Jul 13
Federated Low-Rank Koopman Learning for Multivariate Time-Series Anomaly Detection in IoT Systems
arXiv:2607. 08978v1 Announce Type: new Abstract: Distributed IoT systems generate multivariate time-series streams for monitoring physical assets, servers, and embedded sensing platforms.
arXiv Machine Learning
Jun 16
Anomaly Detection via Mean Shift Density Enhancement
arXiv:2602. 03293v2 Announce Type: replace Abstract: Unsupervised anomaly detection stands as an important problem in machine learning.
arXiv AI
Jun 30
RGLD: Randomized Global-Local Density Estimation for Tabular Anomaly Detection
arXiv:2606. 28970v1 Announce Type: cross Abstract: Unsupervised tabular anomaly detection requires methods that are accurate, robust across heterogeneous datasets, and computationally efficient.
arXiv AI
Jul 7
Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN
arXiv:2607. 02981v1 Announce Type: cross Abstract: Recent advancements in the Internet of Things (IoT) emphasize the urgent need for advanced network security, as IoT networks feature dynamic topologies, imbalanced traffic, and complex attack patterns.