Phishing Detection in Ethereum Via Temporal Graph Contrastive Learning

Blockchain and decentralized finance have revolutionized the financial ecosystem while simultaneously exposing it to cryptocurrency phishing attacks. Existing phishing detection methods primarily rely on graph learning, but they face significant limitations. Static graph learning approaches fail ...

PROVEX: Enhancing SOC Analyst Trust with Explainable Provenance-Based IDS

Modern intrusion detection systems IDS leverage graph neural networks GNNs to detect malicious activity in system provenance data, but their decisions often remain a black box to analysts. This paper presents a comprehensive XAI framework designed to bridge the trust gap in Security Operations...

Amatriciana: Exploiting Temporal GNNs for Robust and Efficient Money Laundering Detection

Money laundering is a financial crime that poses a serious threat to financial integrity and social security. The growing number of transactions makes it necessary to use automatic tools that help law enforcement agencies detect such criminal activity. In this work, we present Amatriciana, a nove...