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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zheng, Jinkai Luan, Tom H. Zhao, Jinwei Li, Guanjie Zhang, Yao Pan, Jianping Cheng, Nan Dongguan University of Technology School of Electrical Engineering and Intelligentization Guangdong China Xidian University School of Cyber Engineering Shaanxi China University of Victoria Department of Computer Science VictoriaBC Canada Northwestern Polytechnical University School of Computer Science Shaanxi China Xidian University School of Telecommunications Engineering Shaanxi China

ISBN: (纸本)9798350351255

The rapid development of Low Earth Orbit (LEO) satellite networks has provided ubiquitous Internet access to users around the world, especially in areas where there are no terrestrial networks. However, a dish can only communicate with one of the available satellites when uploading data in the current framework, resulting in low communication efficiency. As the number of satellites continues to increase, the current framework cannot make full use of the user-satellite link resources. In this paper, we first conduct a measurement of Starlink's network performance and report some unique features. Then, we propose an adaptive multi-link data allocation framework for LEO satellite networks where a dish can communicate with multiple satellites at the same time to improve data transmission efficiency. With this framework, data can be split into chunks and uploaded simultaneously over multiple links. Our goal is to determine the data allocation strategies to jointly optimize the transmission latency and data processing costs. To this end, we propose a deep reinforcement learning-based algorithm integrated with the traffic prediction module to determine the optimal data allocation strategies in a dynamic network environment. Through extensive simulations, we demonstrate the effectiveness of our approach compared with baselines. © 2024 IEEE.

关键词： Satellite links

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AI technology for the Practices of Societal Sustainable Development

AI Technology for the Practices of Societal Sustainable Deve...

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Computational Intelligence for security, Communication and Sustainable Development (CISCSD), International Conference on

作者： N Santhosh Kumar CH Rajendra Prasad K Pavan Kumar P Department of Computer Science and Engineering ANURAG Engineering College Kodad Telengana India Department of Computer Science and Engineering (Cyber Security) Institute of Aeronautical Engineering Hyderabad India Department of Computer Science and Engineering (AI & ML) Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798350365405

ISBN: (纸本)9798350365412

The importance of incorporating AI technology into the engineering process is growing as technology gets more integrated. In this paper, we will explore artificial intelligence (AI) and its applications in sustainability engineering and management, specifically looking at how AI may improve efficiency, optimize the use of resources, and have a lasting impact. The primary focus is on addressing sustainable sustainability through the use of AI technology in civil, medical, agricultural, and mechanical engineering fields, among others. AI is a symbol of cutting-edge technology that has a significant influence on the growth of industries, the decrease of waste, and the state of the environment. The purpose of this study is to review the literature in search of important trends and insights, and then to investigate how artificial intelligence might be used to encourage sustainable practices in the workplace. From optimizing a flexible workforce to intelligently planning tasks and managing risks.

关键词： Industries Reviews Employment Symbols Market research Planning security Artificial intelligence Sustainable development Mechanical engineering

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FeDiSa: A Semi-asynchronous Federated Learning Framework for Power System Fault and cyberattack Discrimination

FeDiSa: A Semi-asynchronous Federated Learning Framework for...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Husnoo, M.A. Anwar, A. Reda, H.T. Hosseinzadeh, N. Islam, S.N. Mahmood, A.N. Doss, R. School of IT Centre for Cyber Security Research and Innovation Deakin University Geelong Australia School of Engineering Centre for Smart Power and Energy Research Deakin University Geelong Australia Latrobe University Department of Computer Science & IT Melbourne Australia

ISBN: (纸本)9781665494274

With growing security and privacy concerns in the Smart Grid domain, intrusion detection on critical energy infrastructure has become a high priority in recent years. To remedy the challenges of privacy preservation and decentralized power zones with strategic data owners, Federated Learning (FL) has contemporarily surfaced as a viable privacy-preserving alternative which enables collaborative training of attack detection models without requiring the sharing of raw data. To address some of the technical challenges associated with conventional synchronous FL, this paper proposes FeDiSa, a novel Semi-asynchronous Federated learning framework for power system faults and cyberattack Discrimination which takes into account communication latency and stragglers. Specifically, we propose a collaborative training of deep auto-encoder by Supervisory Control and Data Acquisition sub-systems which upload their local model updates to a control centre, which then perform a semi-asynchronous model aggregation for a new global model parameters based on a buffer system and a preset cut-off time. Experiments on the proposed framework using publicly available industrial control systems datasets reveal superior attack detection accuracy whilst preserving data confidentiality and minimizing the adverse effects of communication latency and stragglers. Furthermore, we see a 35% improvement in training time, thus validating the robustness of our proposed method. © 2023 IEEE.

关键词： Anomaly detection

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Low-Complexity PSCL Decoding of Polar Codes

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IEEE Transactions on Communications 2025年

作者： Yao, Xinyuanmeng Ma, Xiao Ningbo University of Technology School of Cyber Science and Engineering Ningbo315211 China Sun Yat-sen University School of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Guangzhou510006 China School of Computer Science and Engineering China Sun Yat-sen University Guangdong Province Key Laboratory of Information Security Technology Guangzhou510006 China

Successive cancellation list (SCL) decoding enables polar codes and their generalizations to deliver satisfactory performance in finite-length scenarios but it comes with high latency and complexity. To reduce latency, a partitioned SCL (PSCL) decoding algorithm, implemented over a PSCL decoding tree, can be utilized. In this work, we aim to lower down the complexity of the PSCL decoding, resulting in an efficient decoding algorithm with low latency and complexity for polar-like codes. To achieve this, we define two metrics at each level of the PSCL decoding tree. One is for evaluating the reliability of a path and the other is for evaluating the reliability of a list of paths. Then, we propose a double-threshold strategy in the PSCL decoding process where unreliable valid paths are pruned based on the first metric, and then the smallest reliable list of paths is selected to continue based on the second metric. Simulation results demonstrate that when polar/CRC-polar/PAC codes are decoded using the proposed low-complexity PSCL decoder, both the sorting complexity and the computational complexity are reduced and significantly decrease as the signal-to-noise ratio (SNR) increases. © 1972-2012 IEEE.

关键词： Decoding

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HybridBoost Ensemble Model for Enhanced Cervical Cancer Detection: A Multi-Stage Feature Optimization Approach

HybridBoost Ensemble Model for Enhanced Cervical Cancer Dete...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Angel Mathew Julia Punitha Malar Dhas Devapriya Nirmal Varghese Babu Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Early cervical cancer diagnosis drastically improves treatment efficacy, requiring sensitive and effective models for diagnostics. This research offers a new HybridBoost Ensemble Model that integrates cutting-edge preprocessing techniques, feature extraction, feature selection, and classification to boost detection efficiency. The proposed preprocessing pipeline contains TriPhase Augmentation with normalization, grayscale trans- formation, and flipping, in combination with Pentamorphic Transformation through brightness, contrast enhancement, edge detection, Gaussian blur, and rotation to optimize image quality. This unified histogram of oriented gradients (HOG), local binary pattern (LBP), Scale-Invariant Feature Transform (SIFT), and Gabor filters method, enhanced gradient-texture fusion (EGTF), helps to represent feature exhaustively. To reduce the features with dimension reduction and preserve vital information, this uses a Genetic Algorithm (GA) and PCA. The HybridBoost Ensemble Model is a union of logistic regression, random forest, support vector machines, KNN, Adaboost, and GradientBoost classifier, optimized through GridSearchCV. The ensemble ap- proach augments bagging, boosting, stacking, and voting to improve generalization and minimize overfitting. Experimental evaluations exhibit substantial performance improvements over baseline methods measured by various performance metrics.. The proposed approach is an effective, automated, and scalable solution for early cervical cancer detection.

关键词： Support vector machines Stacking Pipelines Transforms Feature extraction Cervical cancer Random forests Genetic algorithms Principal component analysis Overfitting

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Hireblock: Hyperledger-based Human Resource Recruitment System

Hireblock: Hyperledger-based Human Resource Recruitment Syst...

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2022 International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2022

作者： Shah, Kaushal Padhya, Mukti Doshi, Prachi Paliwal, Manish Kaur, Hargeet Pandit Deendayal Energy University Computer Engineering Department Gandhinagar India School of Cyber Security & Digital Forensics National Forensics Sciences University Gandhinagar India

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

Human Resource (HR) plays a vital role in the development of the organization. Existing solutions such as LinkedIn, Indeed, Moster J ***, Internshala, Angel List provides access to great list of candidates. The existing employment-oriented online solutions are following content based filtering method by allowing user to find the right person for the right job and vice-versa. These solutions though automate a large base of talent acquisition process, they do not guarantee candidate and document validation. Lacking of document verification feature leads to many forgery cases. Online identity theft is a huge problem for all online businesses. Document verification is an integral tool in the verification of a customer's identity. Further, the existing career and recruitment services are suffering from data clogging issue. Data clogging force the organization to get rid of unused set of data thereby introducing the problem of data breaches, compromise of privacy. All the above-mentioned problems leads to a never ending tedious cycle of relying on third party vendors/software to resolve various problems. To address aforementioned issues this paper propose a novel approach of creating a Hyperledger-based Human Resource Recruitment Pool - Hireblock. The proposed recruitment model provides document verification, reliability, decentralization, security, and scalability. We have implemented proposed Hyperledger based Hireblock model. © 2022 IEEE.

关键词： Blockchain

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Enhance Capacity Region of Multi-Class IIoT Network by Applying Entanglement Assistance Protocol

IET Quantum Communication

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IET Quantum Communication 2025年第1期6卷

作者： Subhi, Doaa Bacsardi, Laszlo Department of Networked Systems and Services Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary Department of Network Engineering and Cyber Security College of Engineering Al-Iraqia University Baghdad Iraq

In the Industrial Internet of Things (IIoT) context, heterogeneous IIoT nodes need diverse performance requirements, including throughput and quality of service (QoS). These IIoT nodes transmit data over a common shared communication medium. The existing critical challenge arises in efficiently scheduling access to this shared medium among a large number of connected IIoT nodes. To address the challenge of random access in IIoT networks, the power of the entanglement-assisted (EA) protocol was exploited to expand the capacity region boundaries of the shared communication medium, thereby enhancing the throughput and quality-of-service (QoS) requirements of the heterogeneous IIoT network. In the literature, IIoT networks are mainly categorised into two types: centralised and distributed. In this paper, we proposed two distinct models: (1) a centralised multi-class IIoT network based on EA protocol and (2) a distributed multi-class IIoT network based on EA protocol. Next, the authors analytically demonstrated that integrating the EA protocol into both proposed types of multi-class IIoT networks significantly increases the capacity region boundaries compared to the classical reference model, namely slotted ALOHA (SA). Finally, the network performance boundaries were evaluated by analysing the throughput values for different network classes and varying numbers of IIoT nodes. The results demonstrate that, for both proposed models (1) and (2), the transmitted load generated by the IIoT nodes over the shared medium achieves dramatically higher throughput compared to the reference IIoT network based on SA. © 2025 The Author(s). IET Quantum Communication published by John Wiley & Sons Ltd on behalf of The Institution of engineering and technology.

关键词： Quantum communication

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Performance Framework for Virtual Machine Migration in Cloud Computing

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computers, Materials & Continua 2023年第3期74卷 6289-6305页

作者： Tahir Alyas Taher M.Ghazal Badria Sulaiman Alfurhood Munir Ahmad Ossma Ali Thawabeh Khalid Alissa Qaiser Abbas Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan School of Information Technology Skyline University CollegeUniversity City SharjahSharjah1797UAE Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebansaan Malaysia(UKM)Bangi43600SelangorMalaysia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Dean of Student Affairs Skyline University CollegeUniversity City SharjahSharjah1797UAE Networks and Communications Department College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Faculty of Computer and Information Systems Islamic University Madinah Madinah42351Saudi Arabia

In the cloud environment,the transfer of data from one cloud server to another cloud server is called *** can be delivered in various ways,from one data centre to *** research aims to increase the migration performance of the virtual machine(VM)in the cloud *** allow cloud customers to store essential data and ***,server usage has grown dramatically due to the virtualization of computer systems,resulting in higher data centre power consumption,storage needs,and operating *** VMs on one data centre manage share resources like central processing unit(CPU)cache,network bandwidth,memory,and application ***-cloud,VMmigration addresses the performance degradation due to cloud server configuration,unbalanced traffic load,resource load management,and fault situations during data *** speed is influenced by the size of the VM,the dirty rate of the running application,and the latency ofmigration *** a result,evaluating VM migration performance while considering all of these factors becomes a difficult *** effort of this research is to assess migration problems on *** simulation results in Matlab show that if the VMsize grows,themigration time of VMs and the downtime can be impacted by three orders *** dirty page rate decreases,themigration time and the downtime grow,and the latency time decreases as network bandwidth increases during the migration time and post-migration overhead calculation when the VMtransfer is *** the simulated cases of VMs migration were performed in a fuzzy inference system with performance graphs.

关键词： Latency cloud computing dirty page ratio storage migration performance

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Assessing the Effects of Blackhole Attacks on MANET Reliability and security

Assessing the Effects of Blackhole Attacks on MANET Reliabil...

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Decision Aid Sciences and Application (DASA), International Conference on

作者： Amjed Abbas Ahmed Saif Aamer Fadhil Ali Hamza Najim Khattab M. Ali Alheeti Nurhizam Safie Mohd Satar Aisha Hassan Abdallah Hashim Faculty of Information Science and Technology Center for Cyber Security Universiti Kebangsaan Malaysia Bangi Malaysia Department of Computer Techniques Engineering Imam AlKadhum College (IKC) Baghdad Iraq Department of Computer Technical Engineering Imam Al-Kadhum College (IKC) Baghdad Iraq Computer Networking Systems Department College of Computer and Information Technology University of Anbar Anbar Iraq Faculty of Information Science and Technology Center for Cyber Security Universiti Kebangsaan Malaysia (UKM) Bangi Malaysia Department of Electrical and Computer Engineering Faculty of Engineering International Islamic University Malaysia Selangor Malaysia

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

Mobile Ad Hoc Networks (MANETs) are characterized by some important attributes, including infrastructure, mobile, and dynamic nature, which makes them have vast applications in different areas of computer networks. The black hole attack falls among the strongly criticized security threats within the framework of the MANETs. This deceptive behavior captures data packets from a source destined for the malicious node and then declines them when they are received. This paper comprehensively reviews prior studies done to analyze and mitigate black hole attacks in MANETs. Besides this, we will conduct a comparative analysis of the research above projects. The present paper extends prior work by actually performing the black hole assault simulations in a MANETs environment. This is done with the aid of the NS2 simulator and the Ad Hoc On-Demands Distance Vector (AODV) routing protocol. In this study, the black hole attack is analyzed and the consequences of the attack are assessed in terms of the system performance and security. We analyze crucial parameters like end-to-end latency, PDR, packet drop, and throughput to evaluate the consequences of this assault. From our results derived from the analysis of our simulations, we aim to present substantial points to do with the impacts of blackhole attacks on MANETs. In this way, our goal is to participate in the perpetually evolving efforts aimed at enhancing the security and reliability of such dynamic networks.

关键词： computer network reliability Collaboration Throughput Ad hoc networks Vectors security Reliability Mobile computing AODV Mobile ad hoc networks

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MAD-ANET:Malware Detection Using Attention-Based Deep Neural Networks

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computer Modeling in engineering & Sciences 2025年第4期143卷 1009-1027页

作者： Waleed Khalid Al-Ghanem Emad Ul Haq Qazi Tanveer Zia Muhammad Hamza Faheem Muhammad Imran Iftikhar Ahmad Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11362Saudi Arabia Centre of Artificial Intelligence Naif Arab University for Security SciencesRiyadh14812Saudi Arabia School of Arts and Sciences The University of Notre DameSydneyNSW 2007Australia Center for Smart Analytics Institute of InnovationScience and SustainabilityFederation University AustraliaBerwickVIC 3806Australia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

In the current digital era,new technologies are becoming an essential part of our ***,the number ofmalicious software ormalware attacks is rapidly *** is no doubt,themajority ofmalware attacks can be detected by most antivirus ***,such types of antivirus programs are one step behind malicious *** to these dilemmas,deep learning become popular in the detection and classification of malicious ***,researchers have significantly focused on finding solutions for malware attacks by analyzing malicious samples with the help of different techniques and *** this research,we presented a lightweight attention-based novel deep Convolutional Neural Network(DNN-CNN)model for binary and multi-class malware classification,including benign,trojan horse,ransomware,and *** applied the Principal Component Analysis(PCA)technique for feature extraction for binary *** used the Synthetic Minority Oversampling Technique(SMOTE)to handle the imbalanced data during multi-class *** proposed attention-based malware detectionmodel is trained on the benchmarkmalware memory dataset named *** indicate that our model obtained high accuracy for binary and multi-class classification,99.5% and 97.9%,respectively.

关键词： Attention-based CNN malware detection machine learning deep learning classification

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