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Artificial intelligence in physiological characteristics recognition for internet of things authentication

Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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How Secure is Your Website? A Comprehensive Investigation on CAPTCHA Providers and Solving Services

arXiv

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arXiv 2023年

作者： Jin, Rui Huang, Lin Duan, Jikang Zhao, Wei Liao, Yong Zhou, Pengyuan University of Science and Technology of China China Research Center for Data to Cyberspace China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet China Anhui University of Technology China

Completely Automated Public Turing Test To Tell Computers and Humans Apart (CAPTCHA) has been implemented on many websites to identify between harmful automated bots and legitimate users. However, the revenue generated by the bots has turned circumventing CAPTCHAs into a lucrative business. Although earlier studies provided information about text-based CAPTCHAs and the associated CAPTCHA-solving services, a lot has changed in the past decade regarding content, suppliers, and solvers of CAPTCHA. We have conducted a comprehensive investigation of the latest third-party CAPTCHA providers and CAPTCHA-solving services' attacks. We dug into the details of CAPTCHA-As-a-Service and the latest CAPTCHA-solving services and carried out adversarial experiments on CAPTCHAs and CAPTCHA solvers. The experiment results show a worrying fact: most latest CAPTCHAs are vulnerable to both human solvers and automated solvers. New CAPTCHAs based on hard AI problems and behavior analysis are needed to stop CAPTCHA solvers. © 2023, CC BY.

关键词： Websites

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A PUF-Based and Cloud-Assisted Lightweight Authentication for Multi-Hop Body Area Network

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Tsinghua Science and technology 2021年第1期26卷 36-47页

作者： Wang Xiao Tan Jiliang Zhang Yuanjing Zhang Zheng Qin Yong Ding Xingwei Wang the College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China Cyberspace Security Research Center Peng Cheng LaboratoryShenzhen 518000China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic TechnologyGuilin 541004China the College of Computer Science and Engineering Northeastern UniversityShenyang 110004China

Wireless sensor technology plays an important role in the military,medical,and commercial fields *** Body Area Network(WBAN)is a special application of the wireless sensor network in human health monitoring,through which patients can know their physical condition in real time and respond to emergencies on *** reliability,guaranteed by the trust of nodes in WBAN,is a prerequisite for the effective treatment of ***,authenticating the sensor nodes and the sink nodes in WBAN is *** paper proposes a lightweight Physical Unclonable Function(PUF)-based and cloud-assisted authentication mechanism for multi-hop body area networks,which compared with the star single-hop network,can enhance the adaptability to human motion and the integrity of data *** authentication mechanism can significantly reduce the storage overhead and resource loss in the data transmission process.

关键词： Physical Unclonable Function(PUF) hardware security Wireless Body Area Network(WBAN)

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Robot and its living space:A roadmap for robot development based on the view of living space

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Digital Communications and Networks 2021年第4期7卷 505-517页

作者： Xiaofeng Cai Huansheng Ning Sahraoui Dhelim Rongjuan Zhou Tao Zhang Yang Xu Yueliang Wan School of Computer and Communication Engineering University of Science and Technology Beijing Beijing 100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing 100083China School of Foreign Studies University of Science and Technology Beijing Beijing 100083China Key Lab of Information Network Security Ministry of Public SecurityChina Run Technologies Co. LidBeijing China

Robot’s living space Human-robot relationship Robot humanization Hybrid intelligence Human-robot integrated society Robots,as the creation of humans,became an irreplaceable component in human *** the advancement of technologies,robots have become more and more intelligent and have been widely used in many fields,such as disease diagnosis,customer services,healthcare for the older people,and so *** robots made our lives much more convenient than ever before,they also brought many potential risks and challenges in technology,security,and *** better understand the development of robots,we proposed a concept of a robot’s living space and analyzed the role of robots in our *** this paper,we focus on setting a theoretical framework of the robot’s living space to further understand the human-robot *** research in this paper contains three central ***,we interpret the concept of the robot’s living space and the functions of each ***,we analyze and summarize the relative technologies which support robots living well in each ***,we provide advice and improvement measures based on a discussion of potential problems caused by the developments of *** the trend of robots humanization and human-robot society integration,we should seriously consider how to collaborate with intelligent robots to achieve hybrid *** build a harmonious human-robot integrated society,studying the robot’s living space and its relationship with humans is the prerequisite and roadmap.

关键词： Robot's living space Human-robot relationship Robot humanization Hybrid intelligence Human-robot integrated society

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More observation leads to more clarity: Multi-view collaboration network for camouflaged object detection

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Neurocomputing 2025年 645卷

作者： Wang, Fangyan Jiao, Ge Yue, Guowen College of Computer Science and Technology Hengyang Normal University Hengyang Hunan421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal University Hengyang Hunan421002 China Hunan Engineering Research Center of Cyberspace Security Technology and Applications Hengyang Normal University Hengyang Hunan421002 China

Currently, Camouflaged Object Detection (COD) methods often rely on single-view feature perception, which struggles to fully capture camouflaged objects due to environmental interference such as background clutter, lighting variations, and viewpoint changes. To address this, we propose the multi-view collaboration network (MCNet), inspired by human visual strategies for complex scene analysis. MCNet incorporates multiple perspectives for enhanced feature extraction. The global perception module takes the original, far, and near views, using different large-kernel convolutions and multi-head attention mechanisms for global feature embedding. In parallel, the local perception module processes the tilted, projected, and color-jittered views, extracting fine-grained local features through multi-branch deep convolutions and dilated convolutions. To facilitate deep interaction between global and local features, we introduce the hybrid interactive module, which explores the correlation of multi-view feature information and adaptively fuses features. For feature decoding, the dynamic pyramid shrinkage module integrates dynamic gated convolutions with a pyramid shrinkage mechanism, progressively aggregating semantic features through a hierarchical shrinking strategy and group fusion strategy. Experimental results on popular COD benchmark datasets show that MCNet outperforms 18 state-of-the-art methods. © 2025

关键词： Object detection

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pFedCal: Lightweight Personalized Federated Learning with Adaptive Calibration Strategy

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IEEE Transactions on Services Computing 2025年

作者： Deng, Dongshang Wu, Xuangou Zhang, Tao Xiang, Chaocan Zhao, Wei Xu, Minrui Kang, Jiawen Han, Zhu Niyato, Dusit Anhui University of Technology School of Computer Science and Technology Anhui Province Key Laboratory of Digital Twin Technology in Metallurgical Industry Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Ma'anshan243002 China Beijing Jiaotong University School of Cyberspace Science and Technology Beijing100044 China Anhui Provincial Key Laboratory of Digital Twin Technology in Metallurgical Industry Anhui243002 China Chongqing University College of Computer Science Chongqing400044 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore Guangdong University of Technology School of Automation Guangzhou510062 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

Federated learning (FL) is a promising artificial intelligence framework that enables clients to collectively train models with data privacy. However, in real-world scenarios, to construct practical FL frameworks, several challenges have to be addressed, including statistical heterogeneity, constrained resources, and fairness. Therefore, we first investigate an aggregation gap caused by statistical heterogeneity during local model initialization, which not only causes additional computational overhead for clients but also leads to the degradation of fairness. To bridge this gap, we propose pFedCal, a novel personalized federated learning with lightweight adaptive calibration strategy that performs calibration compensation through the prior knowledge of clients. Specifically, we introduce compensation for each client at the model initialization, with the compensation derived from the global gradient and the latest gradient bias. To enhance the calibration effect, we introduce a smoothing-based calibration strategy, and we design an adaptive calibration strategy. A representative example demonstrates that the proposed calibration and smoothing strategies improve fairness for clients. The theoretical analysis indicates that with an appropriate learning rate, pFedCal converges to a first-order stationary point for non-convex loss functions. Comprehensive experimental results show that pFedCal achieves faster convergence, higher accuracy, and improved fairness than the state-of-the-art methods. © 2008-2012 IEEE.

关键词： Federated learning

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Privacy-Preserving Multi-keyword Search over Outsourced Data for Resource-Constrained Devices 2nd

Privacy-Preserving Multi-keyword Search over Outsourced Data...

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2nd International Conference on Blockchain and Trustworthy Systems, Blocksys 2020

作者： Liu, Lin-Gang Zhao, Meng Ding, Yong Wang, Yujue Deng, Hua Wang, Huiyong Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China School of Mechanical and Electrical Engineering Guilin University of Electronic Technology Guilin541004 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin541004 China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen518055 China

ISBN: (纸本)9789811592126

With the rapid development of cloud computing, a variety of cloud-based applications have been developed. Since cloud computing has the features of high capacity and flexible computing, more and more users are motivated to outsource their data to the cloud server for economic savings. Users are able to search over outsourced data according to some keywords with the help of the cloud server. During data searching, the confidentiality of the relevant data could be compromised since the keywords may contain some sensitive information. However, existing privacy-preserving keyword search proposals have high computation complexity, which are not applicable to IoT-related scenarios. That is, the data processing and search trapdoor generation procedures require the users to take resource-intensive computations, e.g., high-dimensional matrix operations, which are unaffordable by resource-constrained devices. To address this issue, we propose a light-weight privacy-preserving multi-keyword search scheme. The security and performance analyses demonstrate that our scheme outperforms existing solutions and is practical in applications. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Energy-Efficient Virtual Machine Placement in Data centers Via Accelerated Genetic Algorithm with Improved Fitness Computation

SSRN

引用

SSRN 2021年

作者： Hormozi, Elham Hu, Shuwen Ding, Zhe Tian, Yu-Chu Wang, You-Gan Yu, Zu-Guo Zhang, Weizhe School of Computer Science Queensland University of Technology GPO Box 2434 BrisbaneQLD4001 Australia School of Mathematical Sciences Queensland University of Technology GPO Box 2434 BrisbaneQLD4001 Australia Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Hunan Xiangtan411105 China School of Cyberspace Science Harbin Institute of Technology Harbin China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen China Queensland University of Technology

Energy efficiency basically refers to how effectively energy can be utilised or consumed by any virtualised data centre giving cloud administrations. Since Genetic Algorithm (GA) is an acceptable method to solve the VM placement problem, this research formulates the Virtual Machine (VM) placement problem as a constrained optimisation problem. A considerable amount of residual resources is available for each new VM allocation, however they are distributed across multiple active PMs, rendering them inefficient. Our research focuses on improving a data centre’s energy and time efficiency by devising a technique to decrease PM’s residual resources and make them more valuable. As the most time consuming element of a GA is calculating the fitness function, this paper presents a new fitness function to simplify the fitness function calculation in GA. The results are compared to the standard GA and First Fit Decreasing (FFD) algorithm. The findings reveals a 8% energy savings for our GA compared to FFD and a 66% reduction in our GA execution time compared to the standard GA for virtualised data centres. The simulation experiments are conducted on small-, medium- and large-scale of the google data centre to demonstrate the effectiveness of our GA. © 2021, The Authors. All rights reserved.

关键词： Energy efficiency

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Group Daily Arrival Prediction Based on A Hybrid Model Combing Autoregressive Integrated Moving Average Model and Back Propagation Neural Network

Group Daily Arrival Prediction Based on A Hybrid Model Combi...

引用

IEEE International Conference on Unmanned Systems (ICUS)

作者： Xiaoling Zhu Xi Zhang Ge Shi Yashen Wang National Engineering Laboratory for Risk Perception and Prevention (RPP) China Academy of Electronics and Information Technology of CETC Beijing China School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications School of Computer Beijing Institute of Technology Beijing China

ISBN: (数字)9781728180250

ISBN: (纸本)9781728180267

To solve the problem of predicting daily arrival number of people belonging to a specific group, this paper proposes a hybrid model which integrates linear component (from ARIMA model) with non-linear component (from BP neural network). The experimental results indicate that, hybrid methodology that has both linear and nonlinear modeling capabilities is a good strategy for practical use.

关键词： Predictive models Neurons Data models Analytical models Urban areas Solid modeling Backpropagation

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