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Endogenous Security of FQn networks: Adaptive System-Level Fault Self-Diagnosis

IEEE Transactions on Reliability 2024年第3期73卷 1659-1668页

作者： Lin, Yuhang Lin, Limei Huang, Yanze Xu, Li Hsieh, Sun-Yuan Fujian Normal University College of Computer and Cyber Security Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou350117 China Fujian University of Technology School of Computer Science and Mathematics Fuzhou350118 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan

Endogenous security has the ability to discover, eliminate, and solve internal security problems and hidden dangers within the network, and is a superior technology to ensure future network security. The t/k-diagnosis strategy, as a strong and adaptive self-diagnosis strategy, is an important part for ensuring endogenous security. Moreover, the folded hypercube (FQn) as a data transmission network (e.g., optical network) topology offers new potential for the construction of large-scale, high data throughput, and low-latency systems, such as computing network, human-cyber-physical systems, and smart grid. However, there are few studies on endogenous security based on FQn networks. Therefore, this article designs an adaptive system-level fault self-diagnosis strategy, namely Fast t/k-Diagnosis Under Maeng-Malek Model (Ftk-DIAG-MM*) to diagnosis the faulty vertices in FQn network under the Maeng-Malek model (MM∗ mod). Then, we provide a proof of the algorithm correctness theoretically by the fault tolerance of FQn network. It is derived by theoretical derivation that the t/k-diagnosability inherent to the FQn network is (n+1)(k+1)-k(k+3)/2. The simulation experiments demonstrate that the designed Ftk-DIAG-MM∗ strategy can correctly diagnose all vertices within the range allowed by the diagnosability, and still has a great performance when it exceeds the range allowed by the diagnosability. It greatly enhances the fault diagnosis capability of FQn network in the circumstance of misdiagnosing a few vertices, which provides an important theoretical basis for the reliability and endogenous security of FQn networks. © 1963-2012 IEEE.

关键词： Topology

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Automated object recognition in high-resolution optical remote sensing imagery

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National Science Review 2023年第6期10卷 38-41页

作者： Yazhou Yao Tao Chen Hanbo Bi Xinhao Cai Gensheng Pei Guoye Yang Zhiyuan Yan Xian Sun Xing Xu Hai Zhang School of Computer Science and Engineering Nanjing University of Science and Technology Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Key Laboratory of Network Information System Technology (NIST) Aerospace Information Research Institute Chinese Academy of Sciences Department of Computer Science and Technology Tsinghua University School of Computer Science and Engineering University of Electronic Science and Technology of China Pazhou Laboratory (Huangpu) School of Mathematics Northwest University

INTRODUCTION With the rapid development of remote sensing technology,high-quality remote sensing images have become widely *** automated object detection and recognition of these images,which aims to automatically locate objects of interest in remote sensing images and distinguish their specific categories,is an important fundamental task in the *** provides an effective means for geospatial object monitoring in many social applications,such as intelligent transportation,urban planning,environmental monitoring and homeland security.

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Innovations and Challenges in Green Cloud Computing via LEED, Ant Colony Optimization 3

Innovations and Challenges in Green Cloud Computing via LEED...

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3rd International Conference on Smart Generation Computing, Communication and networking, SMART GENCON 2023

作者： Rohit, V. Robin Saravanan, S.K. Lenin, J. Nisha, F. Sivakumar, S. Rajmohan, M. Computer Science & Engineering School of Computer Science & Engineering and Information Science Presidency University Karnataka Bengaluru India Saveetha School of Engineering Simats Department of BigData and Network Security Tamil Nadu Chennai India Alliance College of Engineering and Design Alliance University Department of Computer Science & Engineering Karnataka Bengaluru India School of Engineering and Technology Computer Science & Engineering Cmr University Karnataka Bengaluru India Department of Computer Science Tamil Nadu Perambalur India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350319125

This paper investigates how Green Cloud Computing may be made more efficient by combining LEED standards with Ant Colony Optimization (ACO), which is an approach to cloud computing that takes environmental considerations into account. The goal is to maximize resource allocation in data centers while minimizing energy use and carbon impact. The innovative combination of ACO and LEED offers a fresh perspective on achieving a balance between computational performance and environmental sustainability. The goal is to design a system that uses ACO-driven algorithms to optimize energy consumption in cloud infrastructure so it meets LEED standards. Innovative solutions for large-scale cloud settings are needed to address the challenges of scalability and convergence speed that ACO faces. Further, in order to widen the scope, Multi-Objective Optimization (MOO) and Particle Swarm Optimization (PSO) are investigated. While MOO has it tough dealing with competing goals, PSO has trouble striking a balance between the two. We can learn more about how to optimize cloud operations while reducing environmental effect thanks to innovations in these fields. By solving problems in ACO, PSO, and MOO, this study paves the way for Green Cloud Computing to become more efficient and less harmful to the environment. © 2023 IEEE.

关键词： Ant Colony Optimization Challenges Energy Environmental Design Green Cloud Computing Innovations Leadership Resource Allocation Sustain ability Technology

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Prediction of DDoS Flooding Attack using Machine Learning Models 3

Prediction of DDoS Flooding Attack using Machine Learning Mo...

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3rd International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2022

作者： Patil, Pooja S Deshpande, S.L. Hukkeri, Geeta S Goudar, R.H. Siddarkar, Poonam Visvesvaraya Technological University Department of Computer Network Engineering Belagavi India

ISBN: (纸本)9781665456647

Nowadays multifarious types of Distributed Denial of Services attacks occur owing to the rapid growth in technology and also potentially cause harm in Software Defined network architecture. As a consequence, it is found one among the crucial and commonly occurring cyber-Attack. There are many traditional and advanced methods for detecting these attacks. This paper intends to build a Machine Learning based model for predicting the DDoS Flooding attacks. The DDoS flooding attacks to be anticipated are involved with numerous types. The ML models used to classify these attacks are namely, Logistic Regression, K-nearest neighbour, Multi-Layer Perceptron, and, Decision Tree classifiers. The implementation is been done with a jupyter notebook with required python packages installed. Among these four classifiers, KNN and Decision Tree Classifiers have shown almost similar and best accuracy of 99.98 percent in TCP and ICMP flooding attack prediction. The Decision Tree Classifier has shown the best accuracy of 77.23 percent compared to others in UDP flooding attack prediction. © 2022 IEEE.

关键词： Denial-of-service attack

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An AI-Assisted Skincare Routine Recommendation System in XR 7th

An AI-Assisted Skincare Routine Recommendation System in XR

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7th International Conference on Artificial Intelligence and Virtual Reality, AIVR 2023

作者： Rajegowda, Gowravi Malalur Spyridis, Yannis Villarini, Barbara Argyriou, Vasileios Department of Network Ands and Digital Media Kingston University London United Kingdom Department of Electronic and Electrical Engineering The University of Sheffield Sheffield United Kingdom School of Computer Science and Engineering University of Westminster London United Kingdom

ISBN: (纸本)9789819990177

In recent years, there has been an increasing interest in the use of artificial intelligence (AI) and extended reality (XR) in the beauty industry. In this paper, we present an AI-assisted skin care recommendation system integrated into an XR platform. The system uses a convolutional neural network (CNN) to analyse an individual's skin type and recommend personalised skin care products in an immersive and interactive manner. Our methodology involves collecting data from individuals through a questionnaire and conducting skin analysis using a provided facial image in an immersive environment. This data is then used to train the CNN model, which recognises the skin type and existing issues and allows the recommendation engine to suggest personalised skin care products. We evaluate our system in terms of the accuracy of the CNN model, which achieves an average score of 93% in correctly classifying existing skin issues. Being integrated into an XR system, this approach has the potential to significantly enhance the beauty industry by providing immersive and engaging experiences to users, leading to more efficient and consistent skincare routines. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Recommender systems

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A Robust Semantic Communication System for Image Transmission

A Robust Semantic Communication System for Image Transmissio...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Peng, Xiang Qin, Zhijin Tao, Xiaoming Lu, Jianhua Letaief, Khaled B. Tsinghua University Department of Electronic Engineering Beijing China Beijing China State Key Laboratory of Space Network and Communications Beijing China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong

ISBN: (纸本)9798350351255

Semantic communications have gained significant attention as a promising approach to address the transmission bottleneck, especially with the continuous development of 6G techniques. Distinct from the well investigated physical channel impairments, this paper focuses on semantic impairments in images, particularly those arising from adversarial perturbations. Specifically, we propose a novel metric for quantifying the intensity of semantic impairment and develop a semantic impairment dataset. Furthermore, we introduce a deep learning enabled semantic communication system, termed as DeepSC-RI, to enhance the robustness of image transmission, which incorporates a multi-scale semantic extractor with a dual-branch architecture for extracting semantics with varying granularity, thereby improving the robustness of the system. The fine-grained branch incorporates a semantic importance evaluation module to identify and prioritize crucial semantics, while the coarse-grained branch adopts a hierarchical approach for capturing the robust semantics. These two streams of semantics are seamlessly integrated via an advanced cross-attention-based semantic fusion module. Experimental results demonstrate the superior performance of DeepSC-RI under various levels of semantic impairment intensity. © 2024 IEEE.

关键词： Image communication systems

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Development of Gamification and Live Coding-Based Programming Learning Platform to Foster Learning Motivation of Vocational Students

Development of Gamification and Live Coding-Based Programmin...

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2023 International Conference on Electrical and Information Technology, IEIT 2023

作者： Hidayat, Wahyu Nur Dwi Prasetyo, Wiji Zahiroh, Ayu Najmatus Maydi Syahri, Esa Pramudheva Afdloly, Mochammad Mu'iz Sari, Rahajeng Kartika Universitas Negeri Malang Department of Electrical Engineering and Informatics Malang Indonesia Smk Negeri 10 Malang Departement of Computer Network Engineering and Telecommunications Malang Indonesia

ISBN: (纸本)9798350327298

The current utilization of e-learning has not been able to increase participation and learning experience and motivate students to achieve educational goals. This research focuses on developing gamification-based learning media in Object-Oriented Programming (OOP) subjects to increase student learning motivation, determine the feasibility level of learning media, and determine the increase in student learning motivation. The method of the research is design thinking It is used to identify and better understand users in the use of applications, find problems faced by users, and can explore new ideas in the creation of appropriate application features in accordance with user needs. The results of expert media validation from the teacher, lecturer, and programmer amounted to 89.78% in the good category, for the material validation results of 91.33% in the very good category. Media use was implemented by 23 students of class XI RPL SMKN 12 Malang. The user trial results showed a percentage of 83.48% with good criteria. The student learning motivation test results showed a percentage of 81.26% with very high criteria. This shows the effect of gamification implementation in fostering student learning motivation. Gamification-based media has been tested for feasibility by media experts, material experts, and students. Therefore, the media is feasible and can be used as a learning support media in object-oriented programming subjects in Vocational High School. © 2023 IEEE.

关键词： Students

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EEG-based epileptic seizure pattern decoding using vision transformer 15

EEG-based epileptic seizure pattern decoding using vision tr...

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15th International Conference on Innovations in Information Technology, IIT 2023

作者： Hireche, Abdelhadi Damseh, Rafat Sirpal, Parikshat Belkacem, Abdelkader Nasreddine United Arab Emirates University College of Information Technology Department of Computer and Network Engineering United Arab Emirates United Arab Emirates University College of Information Technology Department of Computer Science and Software Engineering United Arab Emirates University of Oklahoma School of Electrical and Computer Engineering Gallogly College of Engineering United States

ISBN: (纸本)9798350382396

Epilepsy is a prevalent neurological disorder and has been studied through the analysis of Electroencephalogram (EEG) signals. However, the identification and classification of epileptic seizure patterns remains challenging due to the non-stationary nature of EEG signals and the presence of artifacts. In this paper, we investigate the applicability of a transformer-based deep learning model to classify seizure patterns observed in epileptic patients. We employed the self-Attention mechanism inherent in transformers to capture complex temporal relationships in the EEG recordings. By prepossessing the EEG signals into suitable input sequences and adapting the transformer architecture, we achieved 78.11% in distinguishing between different epileptic seizure patterns. Our findings indicate that the transformer model, with its ability to manage long-range dependencies, offers a robust approach to EEG-based seizure pattern classification. This work is important for building advanced automated diagnostic tools for epilepsy and related neurological disorders. © 2023 IEEE.

关键词： Electroencephalography

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Performance Analysis of SpaceX and OneWeb Satellites in South Korea 15

Performance Analysis of SpaceX and OneWeb Satellites in Sout...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Moon, Taehan Chu, Heonwoo Lee, Wonjae Kim, Jae-Hyun Ajou University Department of Artificial Intelligence Convergence Network Suwon16499 Korea Republic of Ajou University Department of Electrical and Computer Engineering Suwon16499 Korea Republic of

ISBN: (纸本)9798350364637

Low Earth Orbit (LEO) satellites offer significant advantages in terms of extensive coverage and service connectivity. Many researchers are currently focusing on commercial LEO constellations, such as SpaceX and One Web. In particular, Signal-to-Noise Ratio (SNR) and Line-of-Sight (LoS) are crucial for ensuring high-quality service in LEO satellite communications. In this paper, we evaluate a detailed analysis of the SNR and LoS performance between SpaceX and OneWeb. The simulation results show that the LEO satellites satisfy the minimum technical performance requirements in the South Korea region. © 2024 IEEE.

关键词： Orbits

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Cluster-preserving sampling algorithm for large-scale graphs

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Science China(Information Sciences) 2023年第1期66卷 60-76页

作者： Jianpeng ZHANG Hongchang CHEN Dingjiu YU Yulong PEI Yingjun DENG National Digital Switching System E&T Research Center Information Engineering University Network Systems Department of the Strategic Support Force School of Computer Science and Technology Eindhoven University of Technology Center for Applied Mathematics Tianjin University

Graph sampling is a very effective method to deal with scalability issues when analyzing largescale graphs. Lots of sampling algorithms have been proposed, and sampling qualities have been quantified using explicit properties(e.g., degree distribution) of the sample. However, the existing sampling techniques are inadequate for the current sampling task: sampling the clustering structure, which is a crucial property of the current networks. In this paper, using different expansion strategies, two novel top-leader sampling methods(i.e., TLS-e and TLS-i) are proposed to obtain representative samples, and they are capable of effectively preserving the clustering structure. The rationale behind them is to select top-leader nodes of most clusters into the sample and then heuristically incorporate peripheral nodes into the sample using specific expansion strategies. Extensive experiments are conducted to investigate how well sampling techniques preserve the clustering structure of graphs. Our empirical results show that the proposed sampling algorithms can preserve the population's clustering structure well and provide feasible solutions to sample the clustering structure from large-scale graphs.

关键词： graph sampling clustering structure top-leader nodes expansion strategies large-scale graphs

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