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Efficient Modeling Attack on Multiplexer PUFs via Kronecker Matrix Multiplication

IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Wang, Hongfei Wan, Caixue Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China

The Physical Unclonable Function (PUF) is valued for its lightweight nature and unique functionality, making it a common choice for securing hardware products requiring authentication and key generation mechanisms. In response to the susceptibility of individual PUFs to modeling attacks, advanced PUF variants have been developed to improve security measures. One notable type in this regard is the multiplexer-based composition of arbiter PUFs, known as MPUF, which aims to meet high reliability and security standards simultaneously. Current research on attacking MPUF encounters challenges such as substantial demands for training CRPs and low success rates. In this work, we propose a novel numerical modeling attack strategy for MPUFs. Using Kronecker products from mathematical perspective, this method precisely describe the MPUF model without using complex network architectures, boosting attack accuracy and overall efficiency. Experiment comparison with state-of-the-art works demonstrates that our method achieves better performance in terms of attack accuracy, robustness, and efficiency. Our method is able to successfully attack a (512, 8)-MPUF in 32.71 minutes with 97.14% accuracy, outperforming all existing attack methods on MPUFs. More, we validate our method through experiments with hardware implementations on FPGAs. The advantages of our method also include the adaptability to be employed to attack other MPUF variations like cMPUF and rMPUF, and the capability to be integrated with an existing attack method for launching efficient attack on MPUFs leveraging reliability information. © 2025 IEEE.

关键词： modeling attack multiplexer-based PUF Physical unclonable function

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TTSlow: Slow Down Text-to-Speech With Efficiency Robustness Evaluations

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 693-704页

作者： Xiaoxue Gao Yiming Chen Xianghu Yue Yu Tsao Nancy F. Chen Institute for Infocomm Research Agency for Science Technology and Research (A*STAR) Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan Department of Electrical Engineering Chung Yuan Christian University Taoyuan City Taiwan

Text-to-speech (TTS) has been extensively studied for generating high-quality speech with textual inputs, playing a crucial role in various real-time applications. For real-world deployment, ensuring stable and timely generation in TTS models against minor input perturbations is of paramount importance. Therefore, evaluating the robustness of TTS models against such perturbations, commonly known as adversarial attacks, is highly desirable. In this paper, we propose TTSlow, a novel adversarial approach specifically tailored to slow down the speech generation process in TTS systems. To induce long TTS waiting time, we design novel efficiency-oriented adversarial loss to encourage endless generation process. TTSlow encompasses two attack strategies targeting both text inputs and speaker embedding. Specifically, we propose TTSlow-text, which utilizes a combination of homoglyphs-based and swap-based perturbations, along with TTSlow-spk, which employs a gradient optimization attack approach for speaker embedding. TTSlow serves as the first attack approach targeting a wide range of TTS models, including autoregressive and non-autoregressive TTS ones, thereby advancing exploration in audio security. Extensive experiments are conducted to evaluate the inference efficiency of TTS models, and in-depth analysis of generated speech intelligibility is performed using Gemini. The results demonstrate that TTSlow can effectively slow down two TTS models across three publicly available datasets.

关键词： Perturbation methods Speech processing Robustness Computational modeling Security Optimization Text to speech Speech recognition Predictive models Real-time systems

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Seamless Distributed Power Management of DC Microgrid under Cyber Attacks with Bidirectional Power Flow

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Tran, Dat Thanh Kim, Kyeong-Hwa Lai, Jih-Sheng Seoul National University of Science and Technology Research Center for Electrical and Information Technology Department of Electrical and Information Engineering Seoul01811 Korea Republic of Department of Electrical and Computer Engineering BlacksburgVA24061 United States

A seamless distributed power management for the dc microgrid (DCMG) with bidirectional power flow is presented in this paper to achieve the overall system stabilization even under severe cyber attacks. First, a distributed secondary control (DSC) based on the V*-P droop curves is presented to ensure the power and voltage regulation for the DCMG system consisting of the electric vehicle (EV), wind turbine, battery, load, and utility grid agents under uncertain conditions. The proposed scheme automatically adjusts the utility grid droop curve to minimize electricity expenditure under electricity price change. To eliminate the negative effect of severe cyber attacks such as false data injection (FDI) and denial-of-service (DoS) in the distributed DCMG system, a resilient DSC based on the compensation term is utilized in the proposed scheme. In addition, by modifying the DSC structure of each power agent, the proposed scheme reduces the overshoot of the dc bus voltage even in the presence of the electricity price change, agent power variation, sudden utility grid disconnection, or critical state-of-charge (SOC) levels. Furthermore, the proposed distributed DCMG system utilizes only the unidirectional digital communication links (DCLs) to cut down the system cost and communication burden, which greatly simplifies communication structure. The efficiency and feasibility of the proposed distributed power management are validated by simulation and experimental results under various conditions. © 2025 IEEE.

关键词： Cyber attacks

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Knowledge Graph Entity Typing with Curriculum Contrastive Learning 31

Knowledge Graph Entity Typing with Curriculum Contrastive Le...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wang, Hao Nuo, Minghua Jiang, Shan College of Computer Science Inner Mongolia University Inner Mongolia Hohhot010021 China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Hohhot010021 China Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology Inner Mongolia Hohhot010021 China

ISBN: (纸本)9798891761964

The Knowledge Graph Entity Typing (KGET) task aims to predict missing type annotations for entities in knowledge graphs. Most recent studies only focus on the structural information from an entity's neighborhood or semantic information from textual representations of entities or relations. In this paper, inspired by curriculum learning and contrastive learning, we propose the CCLET model using the Curriculum Contrastive Learning strategy for KGET, which uses the Pre-trained Language Model (PLM) and the graph model to fuse the entity related semantic and the structural information of the Knowledge Graph (KG) respectively. Our CCLET model consists of two main parts. In the Knowledge Fusion part, we design an Enhanced-MLP architecture to fuse the text of the entity's description, related triplet and tuples;In the Curriculum Contrastive Learning part, we define the difficulty of the training course by controlling the level of added noise, we aim to accurately learn with curriculum contrastive learning strategy from easy to difficult. Our extensive experiments demonstrate that the CCLET model outperforms recent state-of-the-art models, verifying its effectiveness in the KGET task. © 2025 Association for Computational Linguistics.

关键词： Contrastive Learning

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Whole genome transcriptomic profiling reveals distinct sex-specific responses to heat stroke

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Journal of Applied Physiology 2025年第4期138卷 964-978页

作者： Bouchama, Abderrezak Gomez, Maria Abdullah, Mashan L. Mahri, Saeed Al Malik, Shuja Shafi Yezli, Saber Mohammad, Sameer Lehe, Cynthia Abuyassin, Bisher Hoehndorf, Robert Experimental Medicine Department King Abdullah International Medical Research Center King Saud bin Abdulaziz University for Health Sciences Ministry of National Guard Health Affairs Riyadh Saudi Arabia Computational Bioscience Research Center (CBRC) Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Biostatistics Epidemiology and Scientific Computing Department King Faisal Specialist Hospital and Research Centre Riyadh Saudi Arabia Computer Electrical and Mathematical Sciences & Engineering (CEMSE) Division King Abdullah University of Science and Technology Thuwal Saudi Arabia

Heat-related mortality remains health challenges exacerbated by climate change, with sex-based differences in outcomes, yet underlying mechanisms remain poorly understood. This study examined transcriptomic responses to heat exposure in peripheral blood mononuclear cells from 19 patients with heat stroke (HS;8 males, mean age 64.8 ± 6.6 yr;11 females, mean age 49.7 ± 11 yr) and 19 controls (11 males, mean age 48.9 ± 9.6 yr;8 females, mean age 44.9 ± 11.8 yr). At admission, gene expression revealed upregulation of heat shock protein genes, and pathway analysis demonstrated activation of heat shock and unfolded protein responses across both sexes consistent with proteotoxic stress. However, distinct metabolic, oxidative stress, cell cycle control, and immune responses were observed within each sex. Females displayed inhibition of protein synthesis, oxidative phosphorylation, and metabolic pathways, including glucose metabolism, indicative of a hypometabolic state. Males maintained metabolic activity precooling and enhanced adenosine triphosphate production postcooling. Females activated nuclear factor erythroid 2-related factor 2 (NRF2)-mediated oxidative stress responses and inhibited DNA replication and mitosis, potentially mitigating genomic instability, whereas these pathways showed limited regulation in males. Females promoted innate immunity via interleukin (IL)-6, inflammasome, and triggering receptor expressed on myeloid cells 1 (TREM1) signaling, whereas males showed suppression of both innate and adaptive immunity, including IL-12, Th1, and T-cell receptor pathways. Upstream analysis identified over 100 transcription factors in both sexes. Males primarily relied on transcriptional mechanisms, whereas females also exhibited translational regulation via La ribonucleoprotein 1 (LARP1), fragile X messenger ribonucleoprotein 1 (FMR1), insulin-like growth factor 2 mRNA binding protein 1 (IGF2BP1), and eukaryotic translation initiation factor 6 (EIF6). These findin

关键词： heat stroke oxidative phosphorylation proteostasis sex-specific responses transcriptomic

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Quantum-Resistant Secure Communication Protocol for Digital Twin-Enabled Context-Aware IoT-Based Healthcare Applications

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IEEE Transactions on Network Science and engineering 2025年

作者： Bera, Basudeb Das, Ashok Kumar Sikdar, Biplab National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

Digital Twins (DTs) play a crucial role in context-aware Internet of Things (IoT) applications within the healthcare sector, including the industrial healthcare domain, by facilitating the continuous sharing of sensitive and confidential patient data from physical objects in real time. This shared data is essential for treatment planning and decision-making processes, often being accessed remotely by authorized users. However, traditional security mechanisms, which rely on the integer factorization problem (IFP) and the elliptic curve discrete logarithm problem (ECDLP), are vulnerable to quantum attacks using algorithms like Shor's, posing significant risks to data protection. As a result, the healthcare sector faces several security challenges, including the vulnerability of sensitive patient data to cyberattacks, quantum threats, the risk of unauthorized access to medical devices and IoT systems, and the increasing sophistication of cybercriminals exploiting weak authentication methods. To address these issues, we propose a quantum-resistant protocol that safeguards data privacy in DT-enabled IoT healthcare applications, ensures secure transmission of information, maintains patient trust, supports long-term data confidentiality, and protects medical devices and IoT systems from potential breaches. By employing lattice-based cryptographic techniques, particularly the ring learning with errors (RLWE) problem, the proposed scheme effectively addresses contemporary security challenges, including those posed by quantum computing. Real-time experiments conducted on Raspberry Pi 4 devices, along with computational overhead analysis, demonstrate the protocol's efficiency. Additionally, formal security validation using the Scyther tool and security analysis with the RoR model reinforce the robustness of the proposed protocol. A comprehensive comparative evaluation against existing schemes highlights its lightweight, scalable, and efficient nature. Furthermore, performance

关键词： Quantum computers

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FFCI: A Camera and IMU Sensors Based Multi-modal Neural Network for Activity Recognition in Smart Factory

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IEEE Transactions on Consumer Electronics 2025年

作者： Wang, Yujue Niu, Xin Lv, Xianwei Yu, Chen Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China

Worker activity recognition is an important aspect of the construction of smart factory. The development of deep neural networks and the widespread distribution of sensors in the smart factory have brought opportunities for the recognition of workers' activities. The existing methods based on camera and IMU sensors respectively have problems of visual occlusion and difficulty in recognition of similar activities, which result in the reduction of recognition accuracy. Therefore, we propose a feature-level fusion based on camera and IMU sensor (FFCI) for activity recognition in smart factory. The FFCI employs an optimized multi-modal fusion strategy to integrate the visual information and the action information of workers' activities. In order to verify the effectiveness of FFCI, we define a simple yet fine-grained assembly task and collect six common operations. Furthermore, we add visual occlusion and similar activity data to get closer to the smart factory. Finally, we evaluate the FFCI on the collected dataset and achieve a recognition accuracy of 98.17%, demonstrating its effectiveness in accurately classifying workers' activities. © 1975-2011 IEEE.

关键词： Factory automation

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SPViT: Accelerate Vision Transformer Inference on Mobile Devices via Adaptive Splitting and Offloading

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IEEE Transactions on Mobile Computing 2025年

作者： Zhao, Sifan Liu, Tongtong Jin, Hai Yao, Dezhong Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China

The Vision Transformer (ViT), which benefits from utilizing self-attention mechanisms, has demonstrated superior accuracy compared to CNNs. However, due to the expensive computational costs, deploying and inferring ViTs on resource-constrained mobile devices has become a challenge. To resolve this challenge, we conducted an empirical analysis to identify performance bottlenecks in deploying ViTs on mobile devices and explored viable solutions. In this paper, we propose SPViT, an adaptive split and offloading method that accelerates ViT inference on mobile devices. SPViT executes collaborative inference of ViT across available edge devices. We introduce a fine-grained splitting technique for the vision transformer structure. Furthermore, we propose an algorithm based on the Auto Regression model to predict partition latency and adaptive offload partitions. Finally, we design offline and online optimization methods to minimize the computational and communication overhead on each device. Based on real-world prototype experiments, SPViT effectively reduces inference latency by 2.2x to 3.3x across four state-of-the-art models. © 2002-2012 IEEE.

关键词： Regression analysis

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Impulsive Control of Nonlinear Multi-Agent Systems: A Hybrid Fuzzy Adaptive and Event-Triggered Strategy

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IEEE Transactions on Fuzzy Systems 2025年

作者： Han, Fang Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Laboratory Cluster and Grid Computing Laboratory School of Computer Science and Technology Wuhan430074 China

This paper presents a hybrid control approach that integrates an adaptive fuzzy mechanism with an event-triggered impulse strategy to address consensus control challenges in nonlinear Multi-Agent Systems (MASs) with uncertain information, both in leader and leaderless scenarios. Firstly, a fuzzy logic-based adaptive control mechanism is proposed to handle system nonlinearity and uncertainty. This mechanism dynamically adjusts fuzzy control rules in real time, enabling the system to adapt to changing conditions and enhancing its robustness against nonlinear disturbances. Secondly, to alleviate communication overhead and reduce control frequency, an event-triggered impulse strategy is implemented. This strategy activates impulse control only under specific conditions, effectively minimizing control actions while maintaining system consensus. Using Lyapunov stability theory, sufficient conditions for achieving system consensus are established, demonstrating the global consensus and robustness of the MASs under certain criteria. Finally, numerical simulations and experimental evaluations validate the proposed method's effectiveness and its practical advantages. © 2025 IEEE.

关键词： Multi agent systems

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A Triangular Stable Node Network based on Self-supervised Learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Qing Gao, Qian Fan, Jun Zhang, Zhiqiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd. No.1999 Shunhua road Shandong Jinan250101 China

ISBN: (纸本)9798350368741

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN-a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model's effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets. © 2025 IEEE.

关键词： graph contrastive learning graph neural networks recommender systems self-supervised learning

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