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.
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Updated
Aug 15, 2024 - TypeScript
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.
A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques
Anomaly detection related books, papers, videos, and toolboxes
MISP (core software) - Open Source Threat Intelligence and Sharing Platform
A curated list of data mining papers about fraud detection.
A curated list of graph-based fraud, anomaly, and outlier detection papers & resources
A Python Library for Graph Outlier Detection (Anomaly Detection)
Extract and aggregate threat intelligence.
Anomaly Detection on Dynamic (time-evolving) Graphs in Real-time and Streaming manner. Detecting intrusions (DoS and DDoS attacks), frauds, fake rating anomalies.
A tool to detect illegitimate stars from bot accounts on GitHub projects
A Deep Graph-based Toolbox for Fraud Detection
Scanner, signatures and the largest collection of Magento malware
StalkPhish - The Phishing kits stalker, harvesting phishing kits for investigations.
Face recognition & Face liveness detection SDK (Face Detection, Face Landmarks, Face Recognition, Face Liveness, Face Pose, Face Expression, Face attributes)
Reproducible Machine Learning for Credit Card Fraud Detection - Practical Handbook
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!
SDK providing app protection and threat monitoring for mobile devices, available for Flutter, Cordova, Android and iOS.
Slides, scripts and materials for the Machine Learning in Finance Course at NYU Tandon, 2022
Code & Data for "Tabular Transformers for Modeling Multivariate Time Series" (ICASSP, 2021)
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 contribut…
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