Popular GitHub Repositories for Fraud Prevention

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

Anomaly detection related books, papers, videos, and toolboxes

Context aware, pluggable and customizable data protection and de-identification SDK for text and images

A Python Library for Graph Outlier Detection (Anomaly Detection)

Slips, a free software behavioral Python intrusion prevention system (IDS/IPS) that uses machine learning to detect malicious behaviors in the network traffic. Stratosphere Laboratory, AIC, FEL, CVUT in Prague.

A Deep Graph-based Toolbox for Fraud Detection

Reproducible Machine Learning for Credit Card Fraud Detection - Practical Handbook

iPython notebook and pre-trained model that shows how to build deep Autoencoder in Keras for Anomaly Detection in credit card transactions data

Slides, scripts and materials for the Machine Learning in Finance Course at NYU Tandon, 2022

Awesome graph anomaly detection techniques built based on deep learning frameworks. Collections of commonly used datasets, papers as well as implementations are listed in this github repository. We also invite researchers interested in anomaly detection, graph representation learning, and graph anomaly detection to join this project as contributors and boost further research in this area.

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

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

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

Radient turns many data types (not just text) into vectors for similarity search, RAG, regression analysis, and more.

Streaming Anomaly Detection Framework in Python (Outlier Detection for Streaming Data)

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.

Experimental tools to backdoor large language models by re-writing their system prompts at a raw parameter level. This allows you to potentially execute offline remote code execution without running any actual code on the victim's machine or thwart LLM-based fraud/moderation systems.

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

Code for KDD 2020 paper Robust Spammer Detection by Nash Reinforcement Learning

BERT4ETH: A Pre-trained Transformer for Ethereum Fraud Detection (WWW23)