MISP (core software) - Open Source Threat Intelligence and Sharing Platform
#计算机科学#A curated list of data mining papers about fraud detection.
#计算机科学#A Deep Graph-based Toolbox for Fraud Detection
Open source user intelligence platform. Monitor, analyze, and protect your web application against cyberfraud, account threats, and abuse. Get started — free.
#人脸识别#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...
#计算机科学#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
#计算机科学#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 anomalie...
#计算机科学#Protect your SIP Servers from bad actors at https://sentrypeer.org
#计算机科学#A Deep Graph-based Toolbox for Fraud Detection in TensorFlow 2.X
#计算机科学#An Unsupervised Graph-based Toolbox for Fraud Detection
#IOS#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.
#计算机科学#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 throu...
#人脸识别#Face recognition SDK iOS with 3D passive liveness detection (Face Detection, Face Landmarks, Face Recognition, Face Liveness, Face Pose, Face Expression, Face attributes)
#计算机科学#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!