Dual-Attentive-Tree-aware-Embedding
Fraud detector
Develops a machine learning model to classify and rank customs fraud cases based on transaction-level data and tree-based features
DATE: Dual Attentive Tree-aware Embedding for Customs Frauds Detection
61 stars
10 watching
19 forks
Language: HTML
last commit: over 3 years ago
Linked from 1 awesome list
Related projects:
Repository | Description | Stars |
---|---|---|
| An implementation of a graph neural network-based fraud detector designed to counter camouflaged fraudsters | 250 |
| Reproduce experiments and results from a research paper on fraud detection using machine learning algorithms. | 4 |
| Develops and evaluates machine learning models for detecting financial fraud | 195 |
| A toolbox for building and comparing graph neural network-based fraud detection models | 698 |
| Maps deception detection techniques to the ATT&CK framework and provides documentation for security professionals | 287 |
| A framework for simulating customs fraud detection by integrating machine learning models and data from import declarations. | 29 |
| A toolbox for unsupervised graph-based fraud detection using multiple algorithms and techniques | 131 |
| Develops an object detection algorithm using a U-Net architecture to detect vehicles in images and videos from a publicly available dataset | 95 |
| A framework for detecting fraud using a novel neural network approach that learns from benign user data | 24 |
| Detects encrypted files using a fast and memory efficient approach without external dependencies. | 30 |
| Develops a survival analysis-based model to detect fraud early | 34 |
| Develops a system to detect, segment, and rank camouflaged objects in images. | 74 |
| Detects malicious network and host activity using Yara, Snort, and ClamAV signatures. | 213 |
| An AI-powered tool that detects whether news articles are fake or not | 8 |
| A Matlab implementation of simultaneous object detection and segmentation using deep learning techniques. | 96 |