TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs

Chip manufacturing is a complex process, and to achieve a faster time to market, an increasing number of untrusted third-party tools and designs from around the world are being utilized. The use of these untrusted third party intellectual properties IPs and tools increases the risk of adversaries...

CEGA: a Cost-Effective Approach for Graph-Based Model Extraction and Acquisition

Graph Neural Networks GNNs have demonstrated remarkable utility across diverse applications, and their growing complexity has made Machine Learning as a Service MLaaS a viable platform for scalable deployment. However, this accessibility also exposes GNN to serious security threats, most notably...

FicGCN: Unveiling the Homomorphic Encryption Efficiency from Irregular Graph Convolutional Networks

Graph Convolutional Neural Networks GCNs have gained widespread popularity in various fields like personal healthcare and financial systems, due to their remarkable performance. Despite the growing demand for cloud-based GCN services, privacy concerns over sensitive graph data remain significant...

Devil'S Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocols

Graph neural networks GNNs have achieved significant success in graph representation learning and have been applied to various domains. However, many real-world graphs contain sensitive personal information, such as user profiles in social networks, raising serious privacy concerns when graph...

WGLE:Backdoor-Free and Multi-Bit Black-Box Watermarking for Graph Neural Networks

Graph Neural Networks GNNs are increasingly deployed in graph-related applications, making ownership verification critical to protect their intellectual property against model theft. Fingerprinting and black-box watermarking are two main methods. However, the former relies on determining model...

Ai-Driven Vulnerability Analysis in Smart Contracts: Trends, Challenges and Future Directions

Smart contracts, integral to blockchain ecosystems, enable decentralized applications to execute predefined operations without intermediaries. Their ability to enforce trustless interactions has made them a core component of platforms such as Ethereum. Vulnerabilities such as numerical overflows,...

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...

Practical Bayes-Optimal Membership Inference Attacks

We develop practical and theoretically grounded membership inference attacks MIAs against both independent and identically distributed i.i.d. data and graph-structured data. Building on the Bayesian decision-theoretic framework of Sablayrolles et al., we derive the Bayes-optimal membership...

Privacy-Aware Cyberterrorism Network Analysis Using Graph Neural Networks and Federated Learning

Cyberterrorism poses a formidable threat to digital infrastructures, with increasing reliance on encrypted, decentralized platforms that obscure threat actor activity. To address the challenge of analyzing such adversarial networks while preserving the privacy of distributed intelligence data, we...

EC-LDA : Label Distribution Inference Attack against Federated Graph Learning with Embedding Compression

Graph Neural Networks GNNs have been widely used for graph analysis. Federated Graph Learning FGL is an emerging learning framework to collaboratively train graph data from various clients. However, since clients are required to upload model parameters to the server in each round, this provides t...

Co-Evolutionary Defence of Active Directory Attack Graphs Via GNN-Approximated Dynamic Programming

Modern enterprise networks increasingly rely on Active Directory AD for identity and access management. However, this centralization exposes a single point of failure, allowing adversaries to compromise high-value assets. Existing AD defense approaches often assume static attacker behavior, but...

Quantifying the Noise of Structural Perturbations on Graph Adversarial Attacks

Graph neural networks have been widely utilized to solve graph-related tasks because of their strong learning power in utilizing the local information of neighbors. However, recent studies on graph adversarial attacks have proven that current graph neural networks are not robust against malicious...