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Evolution Analysis of Network Attack and Defense Situation Based on Game Theory

Computers, Materials & Continua 2025年第4期83卷 1475-1494页

作者： Haiyan Sun Chenglong Shao Jianwei Zhang Kun Wang Wanwei Huang College of Software Engineering Zhengzhou University of Light IndustryZhengzhou450007China Intelligent Computing Power Research Department Zhengzhou Xinda Advanced Technology Research InstituteZhengzhou450007China

To address the problem that existing studies lack analysis of the relationship between attack-defense game behaviors and situation evolution from the game perspective after constructing an attack-defense model,this paper proposes a network attack-defense game model(ADGM).Firstly,based on the assumption of incomplete information between the two sides of the game,the ADGM model is established,and methods of payoff quantification,equilibrium solution,and determination of strategy confrontation results are ***,drawing on infectious disease dynamics,the network attack-defense situation is defined based on the density of nodes in various security states,and the transition paths of network node security states are ***,the network zero-day virus attack-defense behaviors are analyzed,and comparative experiments on the attack-defense evolution trends under the scenarios of different strategy combinations,interference methods,and initial numbers are conducted using the NetLogo simulation *** experimental results indicate that this model can effectively analyze the evolution of the macro-level network attack-defense situation from the micro-level attack-defense *** instance,in the strategy selection experiment,when the attack success rate decreases from 0.49 to 0.29,the network destruction rate drops by 11.3%,in the active defense experiment,when the interference coefficient is reduced from 1 to 0.7,the network destruction rate decreases by 7%,and in the initial node number experiment,when the number of initially infected nodes increases from 10 to 30,the network destruction rate rises by 3%.

关键词： Network attack-defense situation evolution zero-day virus NetLogo

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Overview of image segmentation methods based on deep learning 3

Overview of image segmentation methods based on deep learnin...

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3rd International Conference on Electronic Information engineering and Data Processing, EIEDP 2024

作者： Wang, Yiwen Department of Software Engineering and Intelligent Technology Tianjin University of Finance and Economics Tianjin300222 China

ISBN: (纸本)9781510680531

Image segmentation is an important branch of computer vision, which has been widely used in many research fields. In order to understand the research status in the field of image segmentation, this paper systematically analyses and compares the traditional image segmentation method and the image segmentation method based on deep learning. Firstly, the development, advantages, and disadvantages of traditional image segmentation methods are summarized according to the three types of segmentation methods based on threshold, edge, and region. Then, the segmentation network models of three commonly used image segmentation methods based on deep learning, namely, fully convolutional neural network, pyramid scene analysis network, and Mask R-CNN, are analyzed. Based on the previous experimental results and the current research progress based on deep learning models, the problems of contour edge optimization and multi-level pixel information fusion in the field of image segmentation are summarized. At the same time, improvement ideas are put forward for the existing problems. Finally, the improvement and development of future image segmentation methods are prospected, which provides a reference for the subsequent research of segmentation methods. © 2024 SPIE.

关键词： Image segmentation

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A Line Complex-Based Evolutionary Algorithm for Many-Objective Optimization

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IEEE/CAA Journal of Automatica Sinica 2023年第5期10卷 1150-1167页

作者： Liang Zhang Qi Kang Qi Deng Luyuan Xu Qidi Wu the Department of Control Science and Engineering Tongji UniversityShanghai 201804 the Jiangsu Province Support Software Engineering R&D Center for Modern Information Technology Application in Enterprise Suzhou 215104China the Shanghai Institute of Intelligent Science and Technology Tongji UniversityShanghai 200092China

In solving many-objective optimization problems(MaO Ps),existing nondominated sorting-based multi-objective evolutionary algorithms suffer from the fast loss of selection *** candidate solutions become nondominated during the evolutionary process,thus leading to the failure of producing offspring toward Pareto-optimal front with *** we find a more effective way to select nondominated solutions and resolve this issue?To answer this critical question,this work proposes to evolve solutions through line complex rather than solution points in Euclidean ***,Plücker coordinates are used to project solution points to line complex composed of position vectors and momentum *** position vectors of the solution points,momentum vectors are used to extend the comparability of nondominated solutions and enhance selection ***,a new distance function designed for high-dimensional space is proposed to replace Euclidean distance as a more effective distancebased *** on them,a novel many-objective evolutionary algorithm(MaOEA)is proposed by integrating a line complex-based environmental selection strategy into the NSGAⅢ*** proposed algorithm is compared with the state of the art on widely used benchmark problems with up to 15 *** results demonstrate its superior competitiveness in solving MaOPs.

关键词： Environmental selection line complex many-objective optimization problems(MaOPs) Plücker coordinate

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ResMHA-Net:Enhancing Glioma Segmentation and Survival Prediction Using a Novel Deep Learning Framework

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Computers, Materials & Continua 2024年第10期81卷 885-909页

作者： Novsheena Rasool Javaid Iqbal Bhat Najib Ben Aoun Abdullah Alharthi Niyaz Ahmad Wani Vikram Chopra Muhammad Shahid Anwar Department of Computer Science School of Engineering and TechnologyIslamic University of Science and TechnologyAwantipora192122India Faculty of Computing and Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia Research Groups in Intelligent Machines National School of Engineers of SfaxUniversity of SfaxSfax3038Tunisia Department of Electrical Engineering College of EngineeringKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyPatiala147004Punjab Department of Electrical and Instrumentation Engineering Thapar Institute of Engineering and TechnologyPatiala147004Punjab Department of AI and Software Gachon UniversitySeongnam-si13120Republic of Korea

Gliomas are aggressive brain tumors known for their heterogeneity,unclear borders,and diverse locations on Magnetic Resonance Imaging(MRI)*** factors present significant challenges for MRI-based segmentation,a crucial step for effective treatment planning and monitoring of glioma *** study proposes a novel deep learning framework,ResNet Multi-Head Attention U-Net(ResMHA-Net),to address these challenges and enhance glioma segmentation ***-Net leverages the strengths of both residual blocks from the ResNet architecture and multi-head attention *** powerful combination empowers the network to prioritize informative regions within the 3D MRI data and capture long-range *** doing so,ResMHANet effectively segments intricate glioma sub-regions and reduces the impact of uncertain tumor *** rigorously trained and validated ResMHA-Net on the BraTS 2018,2019,2020 and 2021 ***,ResMHA-Net achieved superior segmentation accuracy on the BraTS 2021 dataset compared to the previous years,demonstrating its remarkable adaptability and robustness across diverse ***,we collected the predicted masks obtained from three datasets to enhance survival prediction,effectively augmenting the dataset *** features were then extracted from these predicted masks and,along with clinical data,were used to train a novel ensemble learning-based machine learning model for survival *** model employs a voting mechanism aggregating predictions from multiple models,leading to significant improvements over existing *** ensemble approach capitalizes on the strengths of various models,resulting in more accurate and reliable predictions for patient ***,we achieved an impressive accuracy of 73%for overall survival(OS)prediction.

关键词： Glioma MRI segmentation multihead attention survival prediction deep learning

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Optimized Autonomous Computing with Trusted Resilient Data Management for IoT Emerging Networks

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IEEE Sensors Journal 2025年第11期25卷 20778-20785页

作者： Shafique, Ayesha Cheng, Benmao Zhao, Bo Alwakid, Ghadah Naif Haq, Muhammad Inaam Ul Laboratory of Intelligent IOT Technology and Applications in Universities China Jouf University College of Computer and Information Sciences Department of Computer Science Al Jowf Sakaka72341 Saudi Arabia Quanzhou University of Information Engineering School of Computer Science and Software Engineering Quanzhou362000 China

The innovative city network integrates numerous computational and physical components to develop real-time systems. These systems can capture sensor data and distribute it to end stations. Most solutions have been presented based on the centralized computing paradigm, which effectively and systematically increases data flow;however, distributed wireless technologies and heterogeneous network services continue to raise significant research problems. These challenges lower the optimization criteria and reflect communication structure around the network edges. In this research, we proposed a sustainable development for smart networks using efficient big data management with collaborative decisions for network devices. It differs from most existing work in the mentioned aspect. It applies computational lightweight intelligence for forwarding collected data using mobile collectors and reduces the congestion flow between devices on the limited bandwidth of wireless links. Moreover, the energy load is efficiently managed with edge-driven methods, and the incorporation of trusted devices ensures the integrity of the smart network. It also tackles potential communication threats smartly. Based on the experiments conducted in Network Simulator (NS-3), the proposed model enhances the efficacy of smart networks for performance metrics with reliability and effective management of resources in Internet of Things (IoT) network. © 2025 IEEE.

关键词： Mathematical models Computational modeling intelligent sensors Data aggregation Internet of Things Security Routing Load modeling Silicon Data models

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An End-to-End Deep Learning Based Approach for Plant Disease Recognition 6

An End-to-End Deep Learning Based Approach for Plant Disease...

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6th International Conference on Communications, Information System and Computer engineering, CISCE 2024

作者： Guo, Mingyue Li, Qiutong Liu, Deyu Chengdu Neusoft University Department of Intelligent Science and Technology Chengdu China Chengdu Neusoft University Department of Network Engineering Chengdu China Chengdu Neusoft University Department of Software Engineering Chengdu China

ISBN: (纸本)9798350353174

The agricultural industry is essential in providing high-quality food and plays a significant role in growing economies and populations. Plant diseases can cause severe losses in food production and eliminate species diversity. Using automated detection techniques for diagnosing plant diseases can enhance food productivity and minimize economic losses in the early stage. Recent advancements in Deep Neural Networks have significantly enhanced the accuracy of object recognition systems. In this paper, we present an end-to-end deep learning based methodology to recognise different diseases of plants, not limited to specific plant species. We investigate the efficiency of different Visual Transformer models with the transfer learning strategy. The AI Challenger 2018 Crop Disease Detection and PlantVillage datasets are used for training and evaluation. Experimental results indicate the efficacy of our proposed approach in accurately recognizing various plant diseases. © 2024 IEEE.

关键词： Agricultural economics

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Automated Area Based Explosive Trace Detection System

Automated Area Based Explosive Trace Detection System

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2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Ohemu, Monday F. Azeta, Ambrose A. Adeyanju, Ibrahim A. Maidoki, Nuradeen ACETEL-National Open University of Nigria Department of Artiftial Intelligent Nigeria Namibia University of Science and Technology Department of Software Engineering Namibia Federal University Department of Computer Engineering Oye-Ekiti Nigeria Natview Technology Abuja Nigeria

ISBN: (纸本)9798350358155

Explosive based attacks on people and sensitive places in the form of terrorism has become a global challenge that is making organizations such as airports, train station, security agencies and the government to do anything possible to secure the lives of people and some essential infrastructures. Several approaches have been adopted to prevent its occurrence that ranges from the use of animals, analytical means and sensor based approach to manual screaming by human personnel. With the development in the field of Artificial intelligent, efforts are being made to deploy Sensor base system that leverage of machine learning technology to make the system highly accurate and sensitive in detection of explosive trace within an environment. This study presents the architecture of area base explosive trace detection utilizing deep learning model. The experiment shows the concept of area based explosive trace detection from trace properties of Carbon, Nitrogen, Oxygen and Hydrogen (CNOH). The deep leaning model recorded an accuracy of 98% on the explosive trace data. The system uses this information to detect the presence of explosion within an area. © 2024 IEEE.

关键词： Airport security

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Secure Consumer-Centric Demand Response Management in Resilient Smart Grid as Industry 5.0 Application with Blockchain-Based Authentication

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 1370-1379页

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Shamshad, Salman Saleem, Muhammad Asad Omar, Marwan 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 Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan Illinois Institute of Technology Information Technology and Management ChicagoIL60616 United States

The development of the industrial Internet of Things and smart grid networks has emphasized the importance of secure smart grid communication for the future of electric power transmission. However, the current deployment of smart meters and wireless communication methods presents several security concerns that must be addressed to ensure sustainable and resilient smart grid networks for Industry 5.0, with a focus on consumer-centric technologies. In this context, we propose a privacy-focused authentication solution for secure consumer-centric demand response management in smart grid networks, incorporating consumer electronics. Our proposed protocol utilizes blockchain-based authentication to enhance resistance against potential attacks and ensure the integrity of demand response data. Additionally, we have incorporated a PUF to prevent physical attacks. We have conducted informal and formal security analyses to verify the effectiveness of our proposed protocol against various threats and to confirm the session key security. Our comparison study has demonstrated that our protocol provides superior security and added functionalities, with lower computation and communication costs compared to similar protocols. Furthermore, we have measured the computational time for implementing the blockchain component of our protocol while considering the impact of varying numbers of block additions and transactions per block. © 1975-2011 IEEE.

关键词： Authentication

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Multi-layer network embedding on scc-based network with motif

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Digital Communications and Networks 2024年第3期10卷 546-556页

作者： Lu Sun Xiaona Li Mingyue Zhang Liangtian Wan Yun Lin Xianpeng Wang Gang Xu Department of Communication Engineering Institute of Information Science TechnologyDalian Maritime UniversityDalian116026China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province DUT School of Software Technology&DUT-RU International School of Information Science and EngineeringDalian University of TechnologyDalian 116620China College of Information and Communication Engineering Harbin Engineering UniversityHarbin 150001China School of Information and Communication Engineering Hainan UniversityHaikou 570228China State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China

Interconnection of all things challenges the traditional communication methods,and Semantic Communication and Computing(SCC)will become new *** is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based *** previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node ***,the content of semantic information is quite *** graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of ***,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology *** Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node *** verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.

关键词： Semantic communication and computing Multi-layer network Graph neural network Motif

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Bibliometric analysis of artificial intelligence and optical coherence tomography images:research hotspots and frontiers

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International Journal of Ophthalmology(English edition) 2023年第9期16卷 1431-1440页

作者： Hai-Wen Feng Jun-Jie Chen Zhi-Chang Zhang Shao-Chong Zhang Wei-Hua Yang Department of Software Engineering School of SoftwareShenyang University of TechnologyShenyang 110870Liaoning ProvinceChina Department of Computer School of Intelligent MedicineChina Medical UniversityShenyang 110122Liaoning ProvinceChina Shenzhen Eye Hospital Jinan UniversityShenzhen 518040Guangdong ProvinceChina

AIM:To explore the latest application of artificial intelligence(AI)in optical coherence tomography(OCT)images,and to analyze the current research status of AI in OCT,and discuss the future research ***:On June 1,2023,a bibliometric analysis of the Web of Science Core Collection was performed in order to explore the utilization of AI in OCT *** parameters such as papers,countries/regions,citations,databases,organizations,keywords,journal names,and research hotspots were extracted and then visualized employing the VOSviewer and CiteSpace V bibliometric ***:Fifty-five nations reported studies on AI biotechnology and its application in analyzing OCT *** United States was the country with the largest number of published ***,197 institutions worldwide provided published articles,where University of London had more publications than the *** reference clusters from the study could be divided into four categories:thickness and eyes,diabetic retinopathy(DR),images and segmentation,and OCT ***:The latest hot topics and future directions in this field are identified,and the dynamic evolution of AIbased OCT imaging are ***-based OCT imaging holds great potential for revolutionizing clinical care.

关键词： artificial intelligence optical coherence tomography bibliometric deep learning machine learning

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