154 datasets found

The dataset is used for training and testing the DAD:DeepAnomalyDetection algorithm. It contains observations from an ATM card reader device, including energy consumption signals.

DeepHTTP: Semantics-Structure Model with Attention for Anomalous HTTP Traffic Detection and Pattern Mining

The CTU-13 dataset is a network capture dataset containing 13 malware traffic captures, consisting of both benign and malware traffic.

Face image synthesis detection is considerably gaining attention because of the potential negative impact on society that this type of synthetic data brings. In this paper, we...

Anomaly detection in images using multivariate Gaussian and whitening transformation

Cybersecurity Autonomous Machine Learning Platform for Anomaly Detection

The Thyroid dataset is a collection of 3772 instances, each described by 14 features.

The Arrhythmia dataset is a collection of 452 electrocardiogram (ECG) records, each containing 10 minutes of ECG data. The dataset is used for classification tasks, such as...

The proposed method uses Weighted Boxes Fusion (WBF) algorithm to obtain the aggregated annotations with the implicit annotators' agreement as confidence scores. The estimated...

The MVTec and VisA datasets are used for anomaly detection.

The MVTec and VisA datasets are used for few-shot anomaly detection.

The TFDS-RP dataset is a real-time unsupervised anomaly detection dataset with localization, focusing on freight train monitoring applications.

The dataset used in this paper is a real-world arrhythmia dataset for evaluating the resilience of linear classification algorithms under training data attacks.

The Subway Exit dataset contains video clips of people leaving a train station.

The Subway Entrance dataset contains video clips of people waiting to board a train.

The UCSD Pedestrian 2 dataset contains video clips of two different walkways.

The UCSD Pedestrian 1 dataset contains video clips of two different walkways.

A dataset of two years of solar generation data from a real home and a manually generated dataset of multiple solar faults.

The dataset used in this paper is a collection of performance metrics for virtual machines in a microservices-based system.

Spot-the-difference self-supervised pre-training for anomaly detection and segmentation.