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Provably Secure Efficient Key-Exchange Protocol for Intelligent Supply Line Surveillance in Smart Grids

IEEE Internet of Things Journal 2025年第12期12卷 21784-21792页

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Alenazi, Mohammed J.F. Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan 250353 China

Intelligent supply line surveillance is critical for modern smart grids. Smart sensors and gateway nodes are strategically deployed along supply lines to achieve intelligent surveillance. They collect data continuously and transmit it to the control centre in real-time. It enables real-time monitoring, fault detection, and efficient energy management across distribution networks. This advanced surveillance system ensures continuous monitoring of supply lines, detecting anomalies, and optimizing operations to maintain the stability and reliability of the smart grid. However, the reliance of all participating nodes on public communication channels to transmit supply line surveillance data exposes these systems to critical cyber attacks. These cyber attacks include impersonation, physical tampering, ephemeral secret leakage, and desynchronization attacks. To address these issues, existing key-exchange protocols often fail to ensure robust security while imposing high computation and communication overheads, limiting their practicality for resource-constrained environments. Therefore, we propose a secure and efficient key exchange and tamper-resistant authentication protocol using Elliptic Curve Cryptography (ECC) and Physical Unclonable Functions (PUFs). The PUF mechanism provides robust resistance against physical tampering and cloning attacks, ensuring enhanced physical security for supply line devices. We validate the security robustness of the devised protocol using the Random or Real (ROR) model. Furthermore, informal security analysis demonstrates the protocol's robustness in rigorously resisting various attacks, including physical tampering, impersonation, ephemeral secret leakage and desynchronization. Moreover, we determine the performance evaluation that reveals the protocol's superior efficiency compared to competing protocols, achieving significant reductions of 28.64% in computation overhead and 9.96% in communication overhead. © 2014 IEEE.

关键词： Authentication Protocol

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Virtual viewpoint reconstruction utilizing monocular images with complex backgrounds 4

Virtual viewpoint reconstruction utilizing monocular images ...

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4th International Computational Imaging Conference, CITA 2024

作者： Li, Ling-Ling Wang, Tianchen Zhang, Huiling Zhao, Jian Xia, Jun School of Computer Engineering Nanjing Institute of technology No.1 Hongjing Road Jiangsu Nanjing211167 China School of Electronic Science and Engineering Southeast University Nanjing210096 China

ISBN: (纸本)9781510688834

In recent years, significant advancements have been made in the field of three-dimensional light field display, positioning it as one of the most promising display technologies. To enhance the effectiveness and realism of 3D information representation, a dense sequence of virtual viewpoints is required for optimal display. However, the acquisition of high-quality virtual viewpoint sequences poses one of the most significant challenges in this technology. Moreover, when relying solely on a single 2D image as reference information, reconstruction of virtual viewpoints often encounters issues such as ghosting artifacts, contour distortions, blurring effects, and gradient inconsistencies. Therefore, a virtual viewpoint generation algorithm based on large-scale neural networks and local boundary restoration is presented. The target view depth map is generated with precise and distinct boundary information based on large model neural networks. Additionally, an efficient local boundary matching algorithm effectively fills occlusion areas. This approach enables the generation of high-quality dense virtual view sequences even in complex backgrounds, offering significant advantages. © 2025 SPIE.

关键词： Virtual environments

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Innovative Real Estate Management System: Artificial Intelligence-based Segregation 5

Innovative Real Estate Management System: Artificial Intelli...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Joshi, Kapil Sidhu, Kawerinder Singh Malik, Neeru Jadhav, Archana Saini, Dilipkumar Jang Bahadur Siddharth, Dhirendra Uttaranchal University Department of Computer Science & Engineering Uttarakhand Dehradun India Uttaranchal Institute of Management Uttaranchal University Uttarakhand Dehradun India Pimpri Chinchwad University School of Engineering & Technology Maharashtra Pune India Pimpri Chinchwad University School of Engineering and Technology India Department of Computer Science and Engineering Bangalore560 068 India Galgotias University Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798331504403

Information technology has grown very fast and has led to a lot of changes in the property industry, thus demanding businesses to enhance their operational standards. This research study analyses how an advanced Real Estate Management System is designed and executed;this includes creating useful functions for working with data (CRUD operations), and better security measures among others like complex information visualization frameworks. The MERN stack is used here where innovative technologies are employed such as MongoDB as the database system;*** which is a web application framework running on ***;React that allows building user interfaces and finally *** itself being used for server-side scripting enabling asynchronous event-driven I/O. It offers a flexible and responsive structure that can handle huge sets of data common with real estate transactions during different stages. Apart from that, the system has a very broad user authentication method that adheres to the best practices used in the industry for ensuring data integrity and confidentiality. The system also comes with extensive data visualization capabilities by leveraging charting libraries like *** and *** which allow quick viewing of property information, financials as well as market analysis by clients and administrators alike. In impact, the whole thing works as a planning tool, allowing professionals in the property sector to the productivity, safety, and definition of vision necessary to make accurate choices in a rapidly evolving digital context. © 2024 IEEE.

关键词： Information management

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Evaluating Self-Supervised Learning Models for Multi-Label Pathology Detection: A Benchmark Against Supervised Learning 2

Evaluating Self-Supervised Learning Models for Multi-Label P...

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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Agarwal, Nidhi Pooja Kumar, Nitish Anushka Garg, Yashica Department of Computer Science Engineering Galgotias University Greater Noida India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Information Technology Bharati Vidyapeeth College of Engineering New Delhi India Department of Computer Science and Engineering Indira Gandhi Delhi Technical University for Women New Delhi India

ISBN: (纸本)9798331530389

The analysis and objective of the current work is to check the performance of SSL Models in multi-label classification on the available dataset of NIH Chest X-ray images which is a large-scale medical imaging dataset. The performance of SSL models, Dino_v_bl6, Dino with ResNet-50, and MoCo (Momentum Contrast) has been evaluated against the supervised model ResNet. These models are supposed to train to detect multiple pathologies using chest X-ray images. We also note that self-supervised embedding models outperform traditional supervised learning to a significant extent, achieving similar or higher performance on a number of quantitative metrics when compared to the softmax supervised ResNet model. This also draws attention to the possibilities of using SSL for biosignal-specific medical images. Furthermore, SSL approaches offer a promising solution in scenarios where there is an overabundance of annotated datasets available for training models such as deep neural networks. The effectiveness of SSL approaches in addressing the problem of data annotation is also emphasized by this study. © 2025 IEEE.

关键词： Self-supervised learning

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Adversarial Machine Learning for Detecting Advanced Threats Inspired by StuxNet in Critical Infrastructure Networks 12

Adversarial Machine Learning for Detecting Advanced Threats ...

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12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Ahmad, Hafiz Bilal Gao, HaiChang Latif, Naila Aziiz, Abdul Auraangzeb, Muhammad Khan, Muhammad Tanveer School of Computer Science and Technology Xidian University Shaanxi 710071 China School of Telecommunications and Engineering Xidian University Shaanxi 710071 China School of Computer Science and Engineering Southeast University Nanjing China School of Electrical Engineering Southeast University Nanjing China School of Software Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350330366

Inspired by the notorious StuxNet malware, this paper presents a unique and resilient architecture to address the imminent issue of identifying advanced threats within critical infrastructure networks. We use adversarial machine learning techniques to keep ahead of ever-changing threats. Our defense system's fundamental component is a Multi-Layer Perceptron (MLP) architecture that we've dubbed 'StuxNet.' This innovative method combines threat intelligence, anomaly detection, and deep learning to forecast and counter sophisticated attacks. The effectiveness of our method in bolstering the security of critical infrastructure networks is supported by empirical evaluations and real-world case studies. Importantly, the StuxNet-MLP architecture has a 98 percent detection accuracy for sophisticated threats. These results highlight the paramount significance of preventative cybersecurity measures in protecting mission-critical systems and provide useful insights for fending off sophisticated attacks. To ensure a safer and more resilient digital future, this study highlights the need to stay ahead in the continuous battle to secure key infrastructure. © 2024 IEEE.

关键词： Critical infrastructures

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Sensitive Information Protection Model Based on Bayesian Game

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computers, Materials & Continua 2022年第10期73卷 883-898页

作者： Yuzhen Liu Zhe Liu Xiaoliang Wang Qing Yang Guocai Zuo Frank Jiang School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan411201China Hunan Key Laboratory for Service computing and Novel Software Technology Xiangtan411201China College of computer and information engineering Hunan University of Technology and BusinessChangshaChina School of Computer Science and Information Engineering Guangzhou Maritime UniversityGuangzhouChina Hunan Software Vocational and Technical University XiangtanChina School of Engineering and IT University of New South WalesNSWAustralia

A game measurement model considering the attacker’s knowledge background is proposed based on the Bayesian game theory aiming at striking a balance between the protection of sensitive information and the quality of *** quantified the sensitive level of information according to the user’s personalized sensitive information protection *** on the probability distribution of sensitive level and attacker’s knowledge background type,the strategy combination of service provider and attacker was analyzed,and a game-based sensitive information protection model was *** the combination of strategies under Bayesian equilibrium,the information entropy was used to measure the leakage of sensitive ***,in the paper the influence of the sensitive level of information and the attacker’s knowledge background on the strategy of both sides of the game was considered *** on,the leakage of the user’s sensitive information was ***,the feasibility of the model was described by experiments.

关键词： Sensitive information game theory Bayesian equilibrium sensitive level information entropy

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DoS Attack Detection Based on Deep Factorization Machine in SDN

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computer Systems Science & engineering 2023年第5期45卷 1727-1742页

作者： Jing Wang Xiangyu Lei Qisheng Jiang Osama Alfarraj Amr Tolba Gwang-jun Kim School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410114China Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computer Engineering Chonnam National UniversityGwangju61186Korea

Software-Defined Network(SDN)decouples the control plane of network devices from the data *** alleviating the problems presented in traditional network architectures,it also brings potential security risks,particularly network Denial-of-Service(DoS)*** many research efforts have been devoted to identifying new features for DoS attack detection,detection methods are less accurate in detecting DoS attacks against client hosts due to the high stealth of such *** solve this problem,a new method of DoS attack detection based on Deep Factorization Machine(DeepFM)is proposed in ***,we select the Growth Rate of Max Matched Packets(GRMMP)in SDN as detection ***,the DeepFM algorithm is used to extract features from flow rules and classify them into dense and discrete features to detect DoS *** training,the model can be used to infer whether SDN is under DoS attacks,and a DeepFM-based detection method for DoS attacks against client host is *** results show that our method can effectively detect DoS attacks in *** with the K-Nearest Neighbor(K-NN),Artificial Neural Network(ANN)models,Support Vector Machine(SVM)and Random Forest models,our proposed method outperforms in accuracy,precision and F1 values.

关键词： Software-defined network denial-of-service attacks deep factorization machine GRMMP

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Enhancement of High Angular Resolution Diffusion-Weighted Imaging Through Parallel Imaging and Motion Correction 5th

Enhancement of High Angular Resolution Diffusion-Weighted Im...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Sandhya, Prajapati Sreejith, V. Alam, Intekhab Haripriya Department of Electrical Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Manduwala Dehradun Navgaon India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India

ISBN: (纸本)9789819780426

Excessive Angular decision Diffusion-Weighted Imaging (HARDI) is an effective imaging modality that produces high decision pictures of fiber tracts within the brain. Reconstruction of those pictures calls for a huge quantity of computational power and reminiscence, because it acquires appreciably extra diffusion weighted photographs than the conventional diffusion-weighted imaging (DWI) strategies. It makes use of multiple receiver coils in parallel, with every coil measuring the same records set. MC improves the accuracy of HARDI reconstruction by using correcting for motion artifacts. The application of PI and MC along with HARDI allows high-decision reconstruction of fiber tracts inside the mind, with stepped forward sign-to-noise ratio and decreased test instances. When used together, PI and MC also can improve the accuracy of HARDI tractography, providing extra designated records on fiber tract micros. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： measuring movement reconstruction

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Improved Electro Search Algorithm with Intelligent Controller Control System:ESPID Algorithm

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Intelligent Automation & Soft Computing 2023年第3期35卷 2555-2572页

作者： Inayet Hakki Cizmeci Adem Alpaslan Altun Akseki Vocational High School Computer Technologies Department Alanya Alaaddin Keykubat UniversityAntalya07425Turkey Computer Engineering Department Selcuk UniversityFaculty of TechnologyKonya42100Turkey

Studies have established that hybrid models outperform single *** particle swarm algorithm(PSO)-based PID(proportional-integral-derivative)controller control system is used in this study to determine the parameters that directly impact the speed and performance of the Electro Search(ESO)algorithm to obtain the global optimum *** algorithm was created by integrating this system with the ESO *** improved ESPID algorithm has been applied to 7 multi-modal benchmark test *** acquired results were compared to those derived using the ESO,PSO,Atom Search Optimization(ASO),and Vector Space Model(VSM)*** a consequence,it was determined that the ESPID algorithm’s mean score was superior in all ***,while comparing the mean duration value and standard deviations,it is observed that it is faster than the ESO algorithm and produces more accurate results than other *** algorithm has been used for the least cost problem in the production of pressure vessels,which is one of the real-life *** results were compared with ESO,Genetic algorithm and *** was found to be superior to other methods with the least production cost value of 5885.452.

关键词： Electro search algorithm intelligent PID optimization multi-optimization

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Cross-guided Cross-modal Feature Fusion Network for RGB-D Salient Object Detection 8

Cross-guided Cross-modal Feature Fusion Network for RGB-D Sa...

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8th International Conference on Intelligent Informatics and Biomedical Sciences, ICIIBMS 2023

作者： Sun, Chenwang Zhang, Mingqian Shanghai Institute of Technology School of Computer Science and Information Engineering Shang Hai China Shanghai Institute of Technology School of Mechanical Engineering Shang Hai China

ISBN: (纸本)9798350369564

RGB-D salient object detection has been one of the hottest research topics in the field of computer vision in recent years. The research in this field aims to achieve automatic detection and segmentation of salient targets in scenes by combining information from RGB images and depth images. The existing RGB-D salient object detection methods generally operate directly on different modules without considering the complementarity between different modes. And for multi-modal fusion, the differences between different modalities have not been fully explored. In order to better solve the above two problems, we propose a cross-guided cross-modal feature fusion network(CCFFNet). It is composed of a cross-guided feature enhancement (CFE) module and a multi-modal feature fusion (MFF) module. Specifically, in the cross-guided feature enhancement module, the representation of cross modal features is enhanced through guided learning of the mutual feature weights between RGB and depth, fully exploring the complementarity between RGB and depth. In addition, we also utilize multiple different levels of modal features to participate in fusion, enhancing the fusion features through attention, making the model significance prediction of RGB-D salient object detection more accurate. Finally, extensive experiments on five benchmark datasets have shown that our model outperforms the other seven state-of-the-art methods, while also demonstrating its superiority. © 2023 IEEE.

关键词： Object detection

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