QUFIG: GNN-Based Prediction of Quantum Fault Injection Vulnerabilities with Gate-Level Precision

The growing scale and accessibility of quantum hardware exposed new reliability and security challenges in the quantum computing workflow, such as the run-time fault injection attacks in cloud-based quantum computing platforms. However, existing works fail to identify vulnerabilities with...

Enhancing Web Application Firewalls with BERT-GNN for SQL Injection Detection

Detecting sophisticated SQL Injection SQLi attacks remains among the most critical challenges in web applications security. This research study has resulted in an optimised hybrid BERT-GNN pipeline with improved detection accuracy and robustness while reducing false-positive and false-negative...

Defending Network Intrusion Detection Systems Based on Graph Neural Networks against Structural Adversarial Attacks

Graph Neural Networks GNNs represent a promising solution for Machine Learning ML based Network Intrusion Detection Systems NIDS, thanks to their ability to leverage both network flow features and topological patterns. While GNN classifiers demonstrate superior robustness against feature-based...

Pre-Model Representation Failures in GNN-Based Smart Contract Vulnerability Detection

This paper is a failure analysis of the representation layer underlying GNN-based smart contract vulnerability detectors. These systems convert source code into graphs before any learning takes place; if the graph fails to capture the code's semantics, no model improvement can compensate. We...

SLAC: Access-Driven CPU-To-GPU Side-Channel Attacks Via System-Level Cache on Apple Silicon

Modern heterogeneous System-on-Chip designs integrate CPU cores and a GPU that share a last-level cache LLC or system-level cache SLC. This sharing exposes a new cross-domain attack surface, and existing attacks on integrated platforms either exploit coarse-grained cache-occupancy contention or...

Bitcoin Forensic Analysis Uncovers Money Laundering Clusters and Criminal Proceeds

A forensic analysis of a graph dataset containing transactions on the Bitcoin blockchain has revealed clusters associated with illicit activity and money laundering, including detecting criminal proceeds sent to a crypto exchange and previously unknown wallets belonging to a Russian darknet marke...