This paper investigates the use of Federated Learning (FL) for hardware assurance, demonstrating that while FL improves model performance over centralized learning, it remains vulnerable to gradient i…

This paper presents a novel data-free Membership Inference Attack (MIA) that uses gradient inversion on Standard Cell Library Layouts (SCLLs) to reconstruct sensitive hardware images from intercepted…

The paper introduces DECIFR, a novel two-stage Membership Inference Attack (MIA) that exploits standard cell library layouts to reconstruct sensitive IC training data from intercepted federated model…

The paper introduces Dynamic Sharded Federated Learning (DSFL), a secure aggregation framework that significantly reduces communication overhead and enhances update verification for cross-institution…

FedIDM introduces a novel federated learning framework that uses iterative distribution matching to achieve fast and stable convergence and maintain high model utility even when facing a large proport…

EdgeDetect is a communication-efficient and privacy-preserving federated intrusion detection system that uses gradient binarization and homomorphic encryption to significantly reduce bandwidth usage w…

The paper proposes DIST-FL, a distributed system using multiple TEEs and an append-only ledger to enhance the security and robustness of federated learning aggregation against server-side adversaries.

The paper proposes 'mimetic deception,' a novel IP camouflaging technique that structurally disguises a functional IP as a different appearance IP, thereby thwarting both structural reverse engineerin…

This paper systematically evaluates Provenance-based Intrusion Detection Systems (PIDSes) in real industrial scenarios, revealing that existing systems struggle with data heterogeneity, advanced attac…

This paper proposes and evaluates a federated deep learning framework using autoencoders for lightweight, privacy-preserving, and scalable real-time anomaly detection in resource-constrained IoT netwo…

The paper proposes PCDM, a diffusion-based framework that enables highly stealthy and effective data poisoning attacks against Federated Learning systems, significantly degrading global performance wh…

This survey reviews the integration of AI and LLMs into hardware security verification, demonstrating its potential to automate complex stages while stressing the necessity of grounding AI outputs in…

CLAD is a federated learning framework that jointly performs anomaly detection and attack classification in heterogeneous IoT environments by combining clustered learning with a dual-mode architecture…

The paper develops a quantitative scoring system, CRESS, to consistently and comparably rate the severity of novel hardware reverse engineering attack scenarios, proving it is more expressive than ind…

FedFG introduces a robust federated learning framework using flow-matching generation to simultaneously enhance client privacy and defend against sophisticated poisoning attacks.

CIPHR introduces a novel, fine-grain hardware redaction methodology inspired by cryptographic indistinguishability to protect intellectual property against structural attacks that exploit existing art…

This paper investigates how Generative AI enables scalable, hyper-realistic fraud in Chinese e-commerce by fabricating product defect evidence, proposing new defense mechanisms like verifiable materia…

The paper proposes a canonical, end-to-end validation framework to ensure secure integration of Alternative Data Availability (AltDA) systems with Ethereum Layer 2s, demonstrating that L2 integration…

This paper empirically evaluates the effectiveness of Differential Privacy (DP) against Membership Inference Attacks (MIAs) in Federated Learning, demonstrating that a stacking attack strategy can det…

FedDetox introduces a robust framework that sanitizes toxic data on edge devices during federated learning to maintain the safety alignment of Small Language Models (SLMs) without sacrificing utility.