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fraud-detection
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The most advanced browser fingerprinting library.
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 Deep Graph-based Toolbox for Fraud Detection
A tool to detect illegitimate stars from bot accounts on GitHub projects
Scanner, signatures and the largest collection of Magento malware
StalkPhish - The Phishing kits stalker, harvesting phishing kits for investigations.
Reproducible Machine Learning for Credit Card Fraud Detection - Practical Handbook
Face Recognition, Face Liveness Detection, Face Detection, Face Landmarks, Face Compare, Face Matching, Face Pose, Face Expression, Face Attributes, Face Templates Extraction, Face Landmarks
Slides, scripts and materials for the Machine Learning in Finance Course at NYU Tandon, 2022
SDK providing app protection and threat monitoring for mobile devices. Works with Flutter, React Native, Android and iOS. Shield your app with free RASP. Detect reverse engineering, root (Magisk), jailbreak, Frida, emulators, bots, tampering and integrity issues, obfuscation, VPN usage, malware, and monitor device identification and fingerprint.
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)
