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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 3521–3530 of 4856 papers

Title	Date	Tasks	Status
Using Bursty Announcements for Detecting BGP Routing Anomalies	May 14, 2019	Anomaly Detection	—Unverified
Using Causality for Enhanced Prediction of Web Traffic Time Series	Feb 2, 2025	Anomaly DetectionFraud Detection	—Unverified
Using Channel State Information for Physical Tamper Attack Detection in OFDM Systems: A Deep Learning Approach	Nov 6, 2020	Anomaly DetectionSemi-supervised Anomaly Detection	—Unverified
Using Ensemble Classifiers to Detect Incipient Anomalies	Aug 20, 2020	Anomaly DetectionEnsemble Learning	—Unverified
Using Google Analytics to Support Cybersecurity Forensics	Apr 3, 2019	Anomaly DetectionMarketing	—Unverified
Using Kernel SHAP XAI Method to optimize the Network Anomaly Detection Model	Jul 31, 2023	Anomaly DetectionExplainable artificial intelligence	—Unverified
Using Machine Learning for Anomaly Detection on a System-on-Chip under Gamma Radiation	Jan 5, 2022	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Using Semantic Information for Defining and Detecting OOD Inputs	Feb 21, 2023	Anomaly DetectionOut of Distribution (OOD) Detection	—Unverified
Utilizing GANs for Fraud Detection: Model Training with Synthetic Transaction Data	Feb 15, 2024	Anomaly DetectionFace Verification	—Unverified
Utilizing XAI technique to improve autoencoder based model for computer network anomaly detection with shapley additive explanation(SHAP)	Dec 14, 2021	Anomaly DetectionExplainable artificial intelligence	—Unverified

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All datasets MVTec AD VisA MVTec LOCO AD One-class CIFAR-10 CUHK Avenue ShanghaiTech Fishyscapes L&F UCR Anomaly Archive MPDD BTAD One-class CIFAR-100 UBnormal

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	CPR-faster(TensorRT)	FPS	1,016	—	Unverified
2	CPR-fast(TensorRT)	FPS	362	—	Unverified
3	CPR(TensorRT)	FPS	130	—	Unverified
4	UniNet	Detection AUROC	99.9	—	Unverified
5	GLASS	Detection AUROC	99.9	—	Unverified
6	PBAS	Detection AUROC	99.8	—	Unverified
7	HETMM	Detection AUROC	99.8	—	Unverified
8	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	UniNet	Detection AUROC	99.8	—	Unverified
2	GLAD	Detection AUROC	99.5	—	Unverified
3	UniNet(model-unified multi-class)	Detection AUROC	99.15	—	Unverified
4	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	DDAD	Detection AUROC	98.9	—	Unverified
7	GLASS	Detection AUROC	98.8	—	Unverified
8	DiffusionAD	Detection AUROC	98.8	—	Unverified
9	TransFusion	Detection AUROC	98.7	—	Unverified
10	HETMM	Detection AUROC	98.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CSAD	Avg. Detection AUROC	95.3	—	Unverified
2	PSAD	Avg. Detection AUROC	94.9	—	Unverified