SOTAVerified

Anomaly Detection

Anomaly Detection is a binary classification identifying unusual or unexpected patterns in a dataset, which deviate significantly from the majority of the data. The goal of anomaly detection is to identify such anomalies, which could represent errors, fraud, or other types of unusual events, and flag them for further investigation.

[Image source]: GAN-based Anomaly Detection in Imbalance Problems

Papers

Showing 10761100 of 4856 papers

TitleStatusHype
FreCT: Frequency-augmented Convolutional Transformer for Robust Time Series Anomaly Detection0
Quantum Support Vector Regression for Robust Anomaly Detection0
Secure Cluster-Based Hierarchical Federated Learning in Vehicular Networks0
Explainable Machine Learning for Cyberattack Identification from Traffic FlowsCode0
CICADA: Cross-Domain Interpretable Coding for Anomaly Detection and Adaptation in Multivariate Time Series0
LLM-Based Threat Detection and Prevention Framework for IoT Ecosystems0
AI-Driven IRM: Transforming insider risk management with adaptive scoring and LLM-based threat detection0
Enhanced semi-supervised stamping process monitoring with physically-informed feature extraction0
Convolutional Autoencoders for Data Compression and Anomaly Detection in Small Satellite Technologies0
GiBy: A Giant-Step Baby-Step Classifier For Anomaly Detection In Industrial Control Systems0
On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks0
AGATE: Stealthy Black-box Watermarking for Multimodal Model Copyright Protection0
LR-IAD:Mask-Free Industrial Anomaly Detection with Logical ReasoningCode0
The Dark Side of Digital Twins: Adversarial Attacks on AI-Driven Water Forecasting0
Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics0
Performance of Machine Learning Classifiers for Anomaly Detection in Cyber Security ApplicationsCode0
Time and Frequency Domain-based Anomaly Detection in Smart Meter Data for Distribution Network Studies0
Statistical Inference for Clustering-based Anomaly Detection0
Quantum Autoencoder for Multivariate Time Series Anomaly Detection0
Fault Diagnosis in New Wind Turbines using Knowledge from Existing Turbines by Generative Domain Adaptation0
Unsupervised Time-Series Signal Analysis with Autoencoders and Vision Transformers: A Review of Architectures and Applications0
Explainable Unsupervised Anomaly Detection with Random Forest0
Research on Cloud Platform Network Traffic Monitoring and Anomaly Detection System based on Large Language Models0
Blockchain Meets Adaptive Honeypots: A Trust-Aware Approach to Next-Gen IoT Security0
GenCLIP: Generalizing CLIP Prompts for Zero-shot Anomaly Detection0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016Unverified
2CPR-fast(TensorRT)FPS362Unverified
3CPR(TensorRT)FPS130Unverified
4GLASSDetection AUROC99.9Unverified
5UniNetDetection AUROC99.9Unverified
6HETMMDetection AUROC99.8Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8Unverified
8EfficientAD (early stopping)Detection AUROC99.8Unverified
9DDADDetection AUROC99.8Unverified
10PBASDetection AUROC99.8Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8Unverified
2GLADDetection AUROC99.5Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9Unverified
5DDADDetection AUROC98.9Unverified
6Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9Unverified
7DiffusionADDetection AUROC98.8Unverified
8GLASSDetection AUROC98.8Unverified
9TransFusionDetection AUROC98.7Unverified
10HETMMDetection AUROC98.1Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3Unverified
2PSADAvg. Detection AUROC94.9Unverified