The paper proposes a scalable, helper-data-free open-set framework using an OpenGAN-based classifier to unify authentication for diverse and large populations of heterogeneous PUF-based IoT devices.

The paper proposes a resource-efficient, threshold-based authentication scheme for constrained IIoT devices using SRAM PUFs, addressing inherent unreliability through a combination of Hamming code err…

This survey reviews hardware-rooted trust mechanisms, such as PUFs and TPMs, demonstrating that hardware-based solutions are superior to software-only methods for ensuring secure authentication and AI…

This paper proposes a lightweight, multi-layer Machine Learning-based security framework for Industrial IoT (IIoT) to enhance trust convergence and detect advanced threats.

This paper proposes using a fine-tuned foundation model (MOMENT) to detect and classify various attacks in RPL-based IoT networks, achieving performance comparable to state-of-the-art methods.

The paper proposes a constant-time implementation methodology for activation functions on microcontrollers to prevent timing side-channel attacks during embedded neural-network inference.

This paper proposes a lightweight, machine learning-based model for on-device intrusion detection in resource-constrained IoT devices, achieving high detection accuracy for common cyber threats.

This paper enhances an existing autonomous online Intrusion Detection System (AOC-IDS) for IoT by addressing class imbalance, pseudo-label reliability, and computational overhead, achieving significan…

The paper proposes QT-PUF, a novel quantum tunneling leakage-based Physical Unclonable Function (PUF) designed for ultralow-power, implantable IoMT devices, achieving high reliability and minimal powe…

This paper demonstrates the successful application of deep learning-assisted differential fault attacks to three lightweight stream ciphers, achieving high fault location identification accuracies and…

CHRONOS is a hardware-assisted framework that significantly reduces the latency of secure federated learning by decoupling cryptographic key setup from the active training phase, while maintaining hig…

This paper evaluates the security of Universal Circuits (UCs) for hardware obfuscation, demonstrating that they are effective against both oracle-guided and oracle-less attacks.

LiteAtt introduces a verifier-less, Peer-to-Peer Self-Attestation (P2P-SA) framework for modern IoT MCUs, enabling mutual authentication and firmware attestation directly within the connection handsha…

The Device Context Protocol (DCP) introduces a compact, safety-first communication standard designed to allow LLMs to reliably control resource-constrained physical microcontrollers, significantly imp…

LIPPEN introduces a novel hardware-software co-design that provides strong, zero-overhead pointer encryption for enhanced memory safety, achieving comprehensive pointer integrity and confidentiality.

This paper provides the first comprehensive review of threats and defenses specifically targeting on-device AI inference, revealing a significant imbalance where certain attack types, like adversarial…

This paper introduces a unified threat model and evaluation framework to systematically compare privacy-preserving techniques for distributed learning in IoT systems, highlighting the trade-off betwee…

This paper systematically performs a differential fault analysis (DFA) on the lightweight block cipher Lilliput, demonstrating that it is significantly vulnerable to practical fault attacks even under…

The paper introduces ParDef, a generalized defense mechanism that effectively mitigates various types of parameter attacks on deep neural networks while maintaining high performance.

This paper investigates the vulnerability of machine learning-based fault detection and localization systems in Cyber-Physical Systems (CPS) to backdoor attacks, demonstrating that such attacks are su…