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Co-Designed Communication and Computing for Data Reliability in Industrial Cyber-Physical Systems with Cloud-Fog Automation

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IEEE Journal on Selected Areas in Communications 2025年

作者： Fan, Xiaoxuan Deng, Xianjun Liu, Shenghao Zhu, Chenlu Zhou, Xinlei Yi, Lingzhi Wu, Libing Park, Jong Hyuk Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Wuhan Research Institute of Posts and Telecommunications China Zhongnan University of Economics and Law School of Information Engineering China Wuhan University School of Cyber Science and Engineering China Seoul National University of Science and Technology Department of Computer Science and Engineering Korea Republic of

The Cloud-Fog Automation is a newly proposed digital industrial automation architecture aimed at accelerating the integration and collaboration of communication, computing, and control towards next-generation cyber-physical systems (CPSs). Data reliability is one of the key considerations for achieving Cloud-Fog Automation. Sensor nodes serve as infrastructures for data collection within industrial CPSs and are essential for maintaining ultra-high data reliability. However, the underlying sensor nodes communicate frequently, are damage-prone and difficult to identify, which dramatically shortens the network lifetime and poses great challenges to data reliability. Motivated by this fact, this paper co-designs communication architecture, algorithms, and computing models for next-generation industrial CPSs with Cloud-Fog Automation to ensure data reliability and functional security. First, a four-layer energy-efficient communication architecture is proposed and a cluster head computing algorithm based on double deep Q-learning (CH-DDQ) is designed inside the architecture. Besides, a 2-stage hyBrid fault detection scheme (2-Brain) is proposed for underlying sensor nodes. 2-Brain first incorporates the Obstacle Triple Jump Protocol (OTP) and OTP packets to improve hard fault detection performance. Then, an unsupervised sensor reading soft fault detection model (SR-SFD) based on contrastive learning, momentum, and tensor is adopted to learn discriminative representations of sensor readings and identify soft faults. Simulations and a case study in the nuclear power industry manifest CH-DDQ improves the network lifetime by 5.4%484.3% compared to three peer methods, and OTP performs better than baselines by 33.1% on average. Additionally, SR-SFD exhibits high efficiency in sensor soft fault detection and other application scenarios. © 1983-2012 IEEE.

关键词： Cluster computing

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Private blockchain-envisioned security framework for AI-enabled IoT-based drone-aided healthcare services 2

Private blockchain-envisioned security framework for AI-enab...

引用

2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond, DroneCom 2020

作者： Wazid, Mohammad Bera, Basudeb Mitra, Ankush Das, Ashok Kumar Ali, Rashid Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun248 002 India Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad500 032 India School of Intelligent Mechatronics Engineering Sejong University Seoul Korea Republic of

ISBN: (纸本)9781450381055

Internet of Drones (IoD) architecture is designed to support a coordinated access for the airspace using the unmanned aerial vehicles (UAVs) known as drones. Recently, IoD communication environment is extremely useful for various applications in our daily activities. Artificial intelligence (AI)-enabled Internet of Things (IoT)-based drone-aided healthcare service is a specialized environment which can be used for different types of tasks, for instance, blood and urine samples collections, medicine delivery and for the delivery of other medical needs including the current pandemic of COVID-19. Due to wireless nature of communication among the deployed drones and their ground station server, several attacks (for example, replay, man-in-the-middle, impersonation and privileged-insider attacks) can be easily mounted by malicious attackers. To protect such attacks, the deployment of effective authentication, access control and key management schemes are extremely important in the IoD environment. Furthermore, combining the blockchain mechanism with deployed authentication make it more robust against various types of attacks. To mitigate such issues, we propose a private-blockchain based framework for secure communication in an IoT-enabled drone-aided healthcare environment. The blockchain-based simulation of the proposed framework has been carried out to measure its impact on various performance parameters. © 2020 Association for Computing Machinery.

关键词： Authentication

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Practical limitations on robustness and scalability of quantum Internet

arXiv

引用

arXiv 2023年

作者： Sadhu, Abhishek Somayajula, Meghana Ayyala Horodecki, Karol Das, Siddhartha Raman Research Institute Karnataka Bengaluru560080 India Center for Security Theory and Algorithmic Research International Institute of Information Technology Gachibowli Telangana Hyderabad500032 India Department of Computer Science Engineering G Narayanamma Institute of Technology and Science Telangana Hyderabad500104 India Institute of Informatics National Quantum Information Centre Faculty of Mathematics Physics and Informatics University of Gdańsk Wita Stwosza 57 Gdańsk80-308 Poland International Centre for Theory of Quantum Technologies University of Gdańsk Wita Stwosza 63 Gdańsk80-308 Poland

The realization of a desirably functional quantum Internet is hindered by fundamental and practical challenges such as high loss during transmission of quantum systems, decoherence due to interaction with the environment, fragility of quantum states under measurement, etc. We study the implications of these constraints by analyzing the limitations on the scaling and robustness of the quantum Internet. Considering quantum networks, we present practical bottlenecks for secure communication, delegated computing, and resource distribution among end nodes. In particular, we show that the device-independent quantum key distribution (DI-QKD) network of isotropic states with parameter p has a finite diameter depending on p. Motivated by the power of abstraction in graph theory (in association with quantum information theory), that allows us to consider the topology of a network of users spatially separated over long distances around the globe or separated as nodes within the small space over a processor or circuit within a same framework, we consider graph-theoretic quantifiers to assess network robustness and provide critical values of communication lines for viable communication over quantum Internet. We consider some quantum networks of practical interest, ranging from satellite-based networks connecting far-off spatial locations to currently available quantum processor architectures within computers, and analyze their robustness to perform quantum information processing tasks. Some of these tasks form primitives for delegated quantum computing, e.g., entanglement distribution and quantum teleportation, that are desirable for all scale of quantum networks, be it quantum processors or quantum Internet. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph theory

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Towards adversarial patch analysis and certified defense against crowd counting

arXiv

引用

arXiv 2021年

作者： Wu, Qiming Zou, Zhikang Zhou, Pan Ye, Xiaoqing Wang, Binghui Li, Ang School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China Baidu Inc. Shenzhen China The Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Department of Computer Science Illinois Institute of Technology Chicago United States Department of Electrical and Computer Engineering Duke University Durham United States

Crowd counting has drawn much attention due to its importance in safety-critical surveillance systems. Especially, deep neural network (DNN) methods have significantly reduced estimation errors for crowd counting missions. Recent studies have demonstrated that DNNs are vulnerable to adversarial attacks, i.e., normal images with human-imperceptible perturbations could mislead DNNs to make false predictions. In this work, we propose a robust attack strategy called Adversarial Patch Attack with Momentum (APAM) to systematically evaluate the robustness of crowd counting models, where the attacker's goal is to create an adversarial perturbation that severely degrades their performances, thus leading to public safety accidents (e.g., stampede accidents). Especially, the proposed attack leverages the extreme-density background information of input images to generate robust adversarial patches via a series of transformations (e.g., interpolation, rotation, etc.). We observe that by perturbing less than 6% of image pixels, our attacks severely degrade the performance of crowd counting systems, both digitally and physically. To better enhance the adversarial robustness of crowd counting models, we propose the first regression model-based Randomized Ablation (RA), which is more sufficient than Adversarial Training (ADT) (Mean Absolute Error of RA is 5 lower than ADT on clean samples and 30 lower than ADT on adversarial examples). Extensive experiments on five crowd counting models demonstrate the effectiveness and generality of the proposed method. Copyright © 2021, The Authors. All rights reserved.

关键词： Network security

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Incompressibility of H-free edge modification problems: Towards a dichotomy

arXiv

引用

arXiv 2020年

作者： Marx, Dániel Sandeep, R.B. CISPA Helmholtz Center for Information Security Germany Department of Computer Science and Engineering Indian Institute of Technology Dharwad India

Given a graph G and an integer k, the H-free Edge Editing problem is to find whether there exist at most k pairs of vertices in G such that changing the adjacency of the pairs in G results in a graph without any induced copy of H. The existence of polynomial kernels for H-free Edge Editing (that is, whether it is possible to reduce the size of the instance to kO(1) in polynomial time) received significant attention in the parameterized complexity literature. Nontrivial polynomial kernels are known to exist for some graphs H with at most 4 vertices (e.g., path on 3 or 4 vertices, diamond, paw), but starting from 5 vertices, polynomial kernels are known only if H is either complete or empty. This suggests the conjecture that there is no other H with at least 5 vertices where H-free Edge Editing admits a polynomial kernel. Towards this goal, we obtain a set H of nine 5-vertex graphs such that if for every H ∈ H, H-free Edge Editing is incompressible and the complexity assumption NP 6⊆ coNP/poly holds, then H-free Edge Editing is incompressible for every graph H with at least five vertices that is neither complete nor empty. That is, proving incompressibility for these nine graphs would give a complete classification of the kernelization complexity of H-free Edge Editing for every H with at least 5 vertices. We obtain similar result also for H-free Edge Deletion. Here the picture is more complicated due to the existence of another infinite family of graphs H where the problem is trivial (graphs with exactly one edge). We obtain a larger set H of nineteen graphs whose incompressibility would give a complete classification of the kernelization complexity of H-free Edge Deletion for every graph H with at least 5 vertices. Analogous results follow also for the H-free Edge Completion problem by simple complementation. Copyright © 2020, The Authors. All rights reserved.

关键词： Graphic methods

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Heart Disease Prediction Using Machine Learning Method

Heart Disease Prediction Using Machine Learning Method

引用

Cyber Resilience (ICCR), International Conference on

作者： Mafia Rasheed Muhammad Adnan Khan Nouh Sabri Elmitwally Ghassan F. Issa Taher M. Ghazal Hamza Alrababah Beenu Mago Department of Computer Science Agriculture University Faisalabad Pakistan Faculty of Computing Riphah School of Computing & Innovation Riphah International University Lahore Pakistan Faculty of Computing Engineering and the Built Environment Birmingham City University Birmingham United Kingdom School of Information Technology Skyline University College University City Sharjah Sharjah UAE Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM) Bangi Selangor Malaysia School of Information Technology Skyline University College Sharjah UAE

ISBN: (数字)9781665461221

ISBN: (纸本)9781665461238

The heart disease is also known as coronary artery disease, many hearts affecting symptoms that are very common nowadays and causes death. It is a challenging task to diagnose heart diseases without any intelligent diagnosing system. Many researchers did research on it and developed a diagnostic system to diagnose heart diseases and worked on it. The prediction of cardiovascular disease, required a brief medical history of patients, including genetic information. The world is in acute need of a system for predicting heart disease and it became crucial. Data mining and machine learning are common techniques used in the field of health care to process large and complex data. This research paper presents reasons for heart disease and a model based on Machine learning algorithms for prediction.

关键词： Heart Support vector machines Training data Machine learning Medical services Predictive models Cardiovascular diseases

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The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions

arXiv

引用

arXiv 2023年

作者： Sami, H. Hammoud, A. Arafeh, M. Wazzeh, M. Arisdakessian, S. Chahoud, M. Wehbi, O. Ajaj, M. Mourad, A. Otrok, H. Abdel Wahab, O. Mizouni, R. Bentahar, J. Talhi, C. Dziong, Z. Damiani, E. Guizani, M. Concordia Institute for Information Systems Engineering Concordia University MontréalQC Canada Department of Electrical Engineering École de Technologie Supérieure Montréal Canada Department of Software and IT engineering École de Technologie Supérieure Montréal Canada Department of Computer and Software Engineering Polytechnique Montréal Montreal Canada Cyber Security Systems and Applied AI Research Center Department of CSM Lebanese American University Beirut Lebanon Division of Science New York University Abu Dhabi United Arab Emirates Department of EECS Khalifa University Abu Dhabi United Arab Emirates Department of ML Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates

The Metaverse offers a second world beyond reality, where boundaries are non-existent, and possibilities are endless through engagement and immersive experiences using the virtual reality (VR) technology. Many disciplines can benefit from the advancement of the Metaverse when accurately developed, including the fields of technology, gaming, education, art & culture, socialization, commerce, and businesses. Nevertheless, developing the Metaverse environment to its full potential is an ambiguous task that needs proper guidance and directions. Existing surveys on the Metaverse focus only on a specific aspect and discipline of the Metaverse and lack a holistic view of the entire process. Moreover, most surveys refrain from providing detailed guidance about the development process of the metaverse, including its impact on technologies, businesses, existing challenges, and potential research directions due to their lack of a macro and micro perception of such a topic. To this end, a more holistic, multi-disciplinary, in-depth, and academic and industry-oriented review is required to provide a thorough study of the Metaverse development pipeline and fill the gap in existing Metaverse surveys. To address these issues, we present in this survey a novel multi-layered pipeline ecosystem composed of (1) the Metaverse computing, networking, communications and hardware infrastructure, (2) environment digitization, and (3) user interactions. For every layer, we discuss the components that detail the steps of its development. Also, for each of these components, we examine the impact of a set of enabling technologies and empowering domains (e.g., Artificial Intelligence, security & Privacy, Blockchain, Business, Ethics, and Social) on its advancement. In addition, we explain the importance of these technologies to support decentralization, interoperability, user experiences, interactions, and monetization. Our presented study highlights the existing challenges for each component, fo

关键词： Edge computing

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Automatic firmware emulation through invalidity-guided knowledge inference (extended version)

arXiv

引用

arXiv 2021年

作者： Zhou, Wei Guan, Le Liu, Peng Zhang, Yuqing National Computer Network Intrusion Protection Center University of Chinese Academy of Sciences China Department of Computer Science University of Georgia United States College of Information Sciences and Technology The Pennsylvania State University United States School of Cyber Engineering Xidian University China School of Computer Science and Cyberspace Security Hainan University China

Emulating firmware for microcontrollers is challenging due to the tight coupling between the hardware and firmware. This has greatly impeded the application of dynamic analysis tools to firmware analysis. The state-of-the-art work automatically models unknown peripherals by observing their access patterns, and then leverages heuristics to calculate the appropriate responses when unknown peripheral registers are accessed. However, we empirically found that this approach and the corresponding heuristics are frequently insufficient to emulate firmware. In this work, we propose a new approach called µEmu to emulate firmware with unknown peripherals. Unlike existing work that attempts to build a general model for each peripheral, our approach learns how to correctly emulate firmware execution at individual peripheral access points. It takes the image as input and symbolically executes it by representing unknown peripheral registers as symbols. During symbolic execution, it infers the rules to respond to unknown peripheral accesses. These rules are stored in a knowledge base, which is referred to during the dynamic firmware analysis. µEmu achieved a passing rate of 95% in a set of unit tests for peripheral drivers without any manual assistance. We also evaluated µEmu with real-world firmware samples and new bugs were discovered. Copyright © 2021, The Authors. All rights reserved.

关键词： Firmware

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Modular Extremely Large-Scale Array Communication: Near-Field Modelling and Performance Analysis

arXiv

引用

arXiv 2022年

作者： Li, Xinrui Lu, Haiquan Zeng, Yong Jin, Shi Zhang, Rui The National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing210096 China The Purple Mountain Laboratories Nanjing211111 China The Chinese University of Hong Kong Shenzhen Shenzhen Research Institute of Big Data Shenzhen518172 China The Department of Electrical and Computer Engineering National University of Singapore 117583 Singapore

This paper investigates wireless communications based on a new antenna array architecture, termed modular extremely large-scale array (XL-array), where an extremely large number of antenna elements are regularly arranged on a common platform in a modular manner. Each module consists of a flexible/moderate number of antenna elements, and different modules are separated with an inter-module spacing that is typically much larger than the inter-element spacing/signal wavelength for ease of deployment. By properly modelling the variations of signal phase, amplitude and projected aperture across different array modules/elements, we develop the new channel model and analyze the signal-to-noise ratio (SNR) performance of the modular XL-array based communications. Under the practical non-uniform spherical wave (NUSW) model, the closed-form expression of the maximum achievable SNR is derived in terms of key geometric parameters, including the total planar array size, module separation distances along each dimension, as well as the user's location in the three-dimensional (3D) space. Besides, the asymptotic SNR scaling laws are revealed as the number of modules along different dimensions goes to infinity. Moreover, we show that our developed near-field modelling and performance analysis include the existing ones for the collocated XL-array, the far-field uniform plane wave (UPW) model, as well as the one-dimensional (1D) modular extremely large-scale uniform linear array (XL-ULA) as special cases. Extensive simulation results are provided to validate our obtained results. Copyright © 2022, The Authors. All rights reserved.

关键词： Signal to noise ratio

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MedSteGAN: Medical Embedding-Optimized Steganography with Improved GAN

SSRN

引用

SSRN 2023年

作者： Peng, Yuxiang Fu, Chong Zheng, Yu Tian, Yunjia Cao, Guixing Chen, Junxin School of Computer Science and Engineering Northeastern University Liaoning Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Liaoning Shenyang110819 China Department of Information Engineering The Chinese University of Hong Kong Ma Liu Shui Shatin New Territories Hong Kong Institute of Telecommunication Satellite China Academy of Space Technology Beijing100094 China School of Software Dalian University of Technology Liaoning Dalian116621 China

Medical diagnosing systems contain sensitive information, especially the resulting certificate of diagnosis that involves the patient’s privacy, required to be protected carefully. An effective way is to secretly embed privacy-sensitive information into medical-generated images, eventually being viewed as arbitrary images of nobody/nothing in public. The current research line of medical steganography targets simple data-hiding methods, yet remains barren in optimal embedding and stronger robustness. To fill this gap, we propose an end-to-end steganographic framework for medical images. Our idea is to integrate medical-image adaptive steganography with a generative adversarial network (GAN), named MedSteGAN. To improve both robustness, we design an optimized U-shape gen- erator that extracts features more efficiently utilizing larger kernel convolutions and the ReLU6 function. Also, deconvolutions are replaced by upsampling and convolution layers to avoid the checkerboard effect. To enhance security and image quality, we employ visual quality loss and mean squared error (MSE) loss based on the multi-scale structural similarity index metric (MS- SSIM) and the high-pass filters, respectively. Extensive experiments demonstrate that MedSteGAN outperforms state-of-the-art steganographic schemes in terms of robustness and image quality. © 2023, The Authors. All rights reserved.

关键词： Image quality

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