The most advanced browser fingerprinting library.

Anomaly detection related books, papers, videos, and toolboxes

MISP (core software) - Open Source Threat Intelligence and Sharing Platform

A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques

A curated list of data mining papers about fraud detection.

A curated list of graph-based fraud, anomaly, and outlier detection papers & resources

Face recognition SDK Android with 3D passive liveness detection (Face Detection, Face Landmarks, Face Recognition, Face Liveness, Face Pose, Face Expression, Face attributes)

Reproducible Machine Learning for Credit Card Fraud Detection - Practical Handbook

A Deep Graph-based Toolbox for Fraud Detection

Setup end to end demo architecture for predicting fraud events with Machine Learning using Amazon SageMaker

Scanner, signatures and the largest collection of Magento malware

Extract and aggregate threat intelligence.

A Python Library for Graph Outlier Detection (Anomaly Detection)

3D Passive Face Liveness Detection!Supports Face Detection, Face Matching, Face Analysis, Face Sentiment, Face Alignment, Face Identification && Face Verification && Face Representation; Face Reconstruction; Face Tracking; Face Super-Resolution on Android

Liveness detection SDK iOS- iBeta level 2 certified 3D passive liveness detection engine which can detect printed photos, video replay, 3D masks, and deepfake threats

Anomaly Detection on Dynamic (time-evolving) Graphs in Real-time and Streaming manner. Detecting intrusions (DoS and DDoS attacks), frauds, fake rating anomalies.

Code & Data for "Tabular Transformers for Modeling Multivariate Time Series" (ICASSP, 2021)

Detection of Accounting Anomalies using Deep Autoencoder Neural Networks - A lab we prepared for NVIDIA's GPU Technology Conference 2018 that will walk you through the detection of accounting anomalies using deep autoencoder neural networks. The majority of the lab content is based on Jupyter Notebook, Python and PyTorch.

The AMLSim project is intended to provide a multi-agent based simulator that generates synthetic banking transaction data together with a set of known money laundering patterns - mainly for the purpose of testing machine learning models and graph algorithms. We welcome you to enhance this effort since the data set related to money laundering is …

A data science project to predict whether a transaction is a fraud or not.