fraud-prevention

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

A curated list of data mining papers about fraud detection.

tirreno - Open Source Security Analytics. Understand, monitor, and protect your platform from cyberfraud, account threats, and abuse. Get started — free.

A Deep Graph-based Toolbox for Fraud Detection

Face Recognition, Face Liveness Detection, Face Anti-Spoofing, Face Detection, Face Landmarks, Face Compare, Face Matching, Face Pose, Face Expression, Face Attributes, Face Templates Extraction, Face Landmarks

IP Intelligence is a free Proxy VPN TOR and Bad IP detection tool to prevent Fraud, stolen content, and malicious users. Block proxies, VPN connections, web host IPs, TOR IPs, and compromised systems with a simple API. GeoIP lookup available.

Code for CIKM 2020 paper Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters

Obtain a Phone Number full profile including HLR, Reputation, Carrier, Social Media Accounts, Geolocation, Validation, Availabilty, Portability and more.

Find phishing kits which use your brand/organization's files and image.

Repository of Yara rules dedicated to Phishing Kits Zip files

Open source fraud and abuse prevention tools

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.

Protect your SIP Servers from bad actors at https://sentrypeer.org

A Deep Graph-based Toolbox for Fraud Detection in TensorFlow 2.X

An Unsupervised Graph-based Toolbox for Fraud Detection

iOS library for device fingerprinting. Does not require server APIs to work, fully client-side operation. MIT license, no restrictions on usage in production.

A list of disposable email domains, cleaned and validated by scanning MX records.

Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks - A lab we prepared for the KDD'19 Workshop on Anomaly Detection in Finance that will walk you through the detection of interpretable accounting anomalies using adversarial autoencoder neural networks. The majority of the lab content is based on Jupyter Notebook, Python and PyTorch.

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

Can we predict accurately on the skewed data? What are the sampling techniques that can be used. Which models/techniques can be used in this scenario? Find the answers in this code pattern!