Modern & flexible browser fingerprinting library

A Python Toolbox for Scalable Outlier Detection (Anomaly Detection)

MISP (core software) - Open Source Threat Intelligence and Sharing Platform (formely known as Malware Information Sharing Platform)

Scanner, signatures and the largest collection of Magento malware

A curated list of data mining papers about fraud detection.

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

Extract and aggregate threat intelligence.

A tool to detect illegitimate stars from bot accounts on GitHub projects

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

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

A curated list of fraud detection papers using graph information or graph neural networks

A Deep Graph-based Toolbox for Fraud Detection

A collection of research and survey papers of fraud detection mainly in advertising.

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.

ThreatBite is a real-time service that detects unwanted web users.

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

Credit Card Fraud Detection using ML: IEEE style paper + Jupyter Notebook

Python implementation of Benford's Law tests.

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!

Tuning XGBoost hyper-parameters with Simulated Annealing

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.

Graph Explorer

A logistic regression model that detects the fraud in online transactions that can be accesed with a REST API

Machine learning Fraud Detection with SPARK and OCTAVE

Fraud Detection by finding the Person of Interest (POI)

Demo to show keystroke dynamics / keystroke biometrics

Accounting Fraud Detection Using Machine Learning

Insurance fraud claims analysis project

Opensource Fraud Triangle Analytics

Classification of fraudulent credit card transactions.