Amazon-Fraud is a multi-relational graph dataset built upon the Amazon review dataset, which can be used in evaluating graph-based node classification, fraud detection, and anomaly detection models.

Dataset Statistics

Graph Construction

The Amazon dataset includes product reviews under the Musical Instruments category. Similar to this paper, we label users with more than 80% helpful votes as benign entities and users with less than 20% helpful votes as fraudulent entities. we conduct a fraudulent user detection task on the Amazon-Fraud dataset, which is a binary classification task. We take 25 handcrafted features from this paper as the raw node features for Amazon-Fraud. We take users as nodes in the graph and design three relations: 1) U-P-U: it connects users reviewing at least one same product; 2) U-S-V: it connects users having at least one same star rating within one week; 3) U-V-U: it connects users with top 5% mutual review text similarities (measured by TF-IDF) among all users.

To download the dataset, please visit this Github repo. For any other questions, please email ytongdou(AT)gmail.com for inquiry.

Fraud-R1 : A Comprehensive Benchmark for Assessing LLM Robustness Against Fraud and Phishing Inducement

Shu Yang*, Shenzhe Zhu*, Zeyu Wu, Keyu Wang, Junchi Yao, Junchao Wu, Lijie Hu, Mengdi Li, Derek F. Wong, Di Wang† (*Contribute equally, †Corresponding author) 😃 Github | 📜 Project Page | 📝 arxiv ❗️Content Warning: This repo contains examples of harmful language.

  📰 News

2025/02/16: ❗️We have released our evaluation code. 2025/02/16: ❗️We have released our dataset.… See the full description on the dataset page: https://huggingface.co/datasets/Chouoftears/Fraud-R1-LLM-Defense-Fraud-Benchmark.

The dataset contains transactions made by credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.

It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, … V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependent cost-sensitive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.

Given the class imbalance ratio, we recommend measuring the accuracy using the Area Under the Precision-Recall Curve (AUPRC). Confusion matrix accuracy is not meaningful for unbalanced classification.

Yelp-Fraud is a multi-relational graph dataset built upon the Yelp spam review dataset, which can be used in evaluating graph-based node classification, fraud detection, and anomaly detection models.

Dataset Statistics

Graph Construction

The Yelp spam review dataset includes hotel and restaurant reviews filtered (spam) and recommended (legitimate) by Yelp. We conduct a spam review detection task on the Yelp-Fraud dataset which is a binary classification task. We take 32 handcrafted features from SpEagle paper as the raw node features for Yelp-Fraud. Based on previous studies which show that opinion fraudsters have connections in user, product, review text, and time, we take reviews as nodes in the graph and design three relations: 1) R-U-R: it connects reviews posted by the same user; 2) R-S-R: it connects reviews under the same product with the same star rating (1-5 stars); 3) R-T-R: it connects two reviews under the same product posted in the same month.

To download the dataset, please visit this Github repo. For any other questions, please email ytongdou(AT)gmail.com for inquiry.

The recent technological advent of cryptocurrencies and their respective benefits have been shrouded with a number of illegal activities operating over the network such as money laundering, bribery, phishing, fraud, among others. In this work we focus on the Ethereum network, which has seen over 400 million transactions since its inception. Using 2179 accounts flagged by the Ethereum community for their illegal activity coupled with 2502 normal accounts, we seek to detect illicit accounts based on their transaction history using the XGBoost classifier. Using 10 fold cross-validation, XGBoost achieved an average accuracy of 0.963 ( ± 0.006) with an average AUC of 0.994 ( ± 0.0007). The top three features with the largest impact on the final model output were established to be ‘Time diff between first and last (Mins)’, ‘Total Ether balance’ and ‘Min value received’. Based on the results we conclude that the proposed approach is highly effective in detecting illicit accounts over the Ethereum network. Our contribution is multi-faceted; firstly, we propose an effective method to detect illicit accounts over the Ethereum network; secondly, we provide insights about the most important features; and thirdly, we publish the compiled data set as a benchmark for future related works.

Bank Account Fraud (BAF) is a large-scale, realistic suite of tabular datasets. The suite was generated by applying state-of-the-art tabular data generation techniques on an anonymized, real-world bank account opening fraud detection dataset.

List and descriptions of benchmark datasets.

EDINET-Bench

📚 Paper | 📝 Blog | 🧑‍💻 Code EDINET-Bench is a Japanese financial benchmark designed to evaluate the performance of LLMs on challenging financial tasks including accounting fraud detection, earnings forecasting, and industry prediction. This dataset is built leveraging EDINET, a platform managed by the Financial Services Agency (FSA) of Japan that provides access to disclosure documents such as securities reports.

  Notice

June 9, 2025: This dataset was… See the full description on the dataset page: https://huggingface.co/datasets/SakanaAI/EDINET-Bench.

Matanga Darknet — 2025 Access Guide

As internet censorship intensifies, Shadow Marketplaces remain crucial tools for anonymous transactions. Matanga Darknet is one of the most reliable platforms offering secure deals, wide product selection, and user-friendly interface. This article explains how to access Matanga Darknet, its advantages, and security measures for darknet operations.

Current mirrors for Matanga Darknet

Search engines may block darknet resources, so we've compiled official and backup mirrors:

Clearnet mirror (if Tor unavailable):

https://mat2web.top (use with VPN!)

Official .onion address (Tor Browser only):

http://matanzkgpadqndp44ysejfdwehmy4m22mzevmicoth6ebzequny6ayid.onion/

Backup domain (if main domain blocked):

https://matangaweb.com/ (verified URL)

Important! Always verify site's PGP signature and avoid phishing clones.

Additional links|mirrors

Download Tor Browser

https://www.torproject.org/download/

What is Matanga Darknet? Matanga Darknet is a darknet marketplace operating on the Escrow (escrow) model that guarantees transaction security. Key features: - Anonymous payments (BTC+LTC) - No KYC (no identity verification) - Seller rating system and honest reviews

How to access Matanga Darknet? 1. Install Tor Browser (

Advantages of Matanga Darknet over other marketplaces ? Wide selection of high-quality products ? PGP encryption for communications ? Automatic seller payouts

How to avoid scammers? - Verify .onion addresses through forums (Dread, Telegram channels) - Don't follow links from emails/messengers - Use hardware wallets (Ledger, Trezor)

Future of darknet marketplaces With the development of decentralization technologies (Freenet, I2P), Matanga Darknet plans to implement: - Fully p2p trading without a central server - NFT-based transaction guarantees

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When Heterophily Meets Heterogeneity:Challenges and a New Large-Scale Graph Benchmark

Junhong Lin¹, Xiaojie Guo², Shuaicheng Zhang³, Yada Zhu², Dawei Zhou³, Julian Shun¹ ¹ MIT CSAIL, ² IBM Research, ³ Virginia Tech

This repository hosts a subset of datasets from H2GB, a large-scale benchmark suite designed to evaluate graph learning models on heterophilic and heterogeneous graphs. These graphs naturally arise in real-world applications such as fraud detection, malware… See the full description on the dataset page: https://huggingface.co/datasets/junhongmit/H2GB.