Browser fingerprinting library. Accuracy of this version is 40-60%, accuracy of the commercial Fingerprint Identification is 99.5%. V4 of this library is BSL licensed.

Anomaly detection related books, papers, videos, and toolboxes

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

A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)

A curated list of data mining papers about fraud detection.

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

Upgrade your Android app with MiniAiLive's 3D Passive Face Liveness Detection! With our advanced computer vision techniques, you can now enhance security and accuracy on your Android platform. Check out our latest repository containing a demonstration of 2D & 3D passive face liveness detection capabilities. Try it out today!

A Deep Graph-based Toolbox for Fraud Detection

Scanner, signatures and the largest collection of Magento malware

Reproducible Machine Learning for Credit Card Fraud Detection - Practical Handbook

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

Face Recognition Face Liveness Detection Android SDK (Face Detection, Face Landmarks, Face Anti Spoofing, Face Pose, Face Expression, Eye Closeness, Age, Gender and Face Recognition)

Extract and aggregate threat intelligence.

A Python Library for Graph Outlier Detection (Anomaly Detection)

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

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 …

Use AutoAI to detect fraud

StalkPhish - The Phishing kits stalker, harvesting phishing kits for investigations.

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