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In-depth Analysis of Interior Gateway Protocols in a Large-scale Enterprise Topologies 3

In-depth Analysis of Interior Gateway Protocols in a Large-s...

3rd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2023

作者： Al-Awami, Saleh Hussein Tahir Abu Raas, Ali Ben Srity, Emad Awadh University of Benghazi Department of Data Communications and Computer Networks Benghazi Libya

ISBN: (纸本)9798350319897

Interior gateway protocols are among the routing protocols with the fastest expanding usage in networking technologies because they provide features that enable these algorithms to communicate together in a standard and flexible way. Network devices communicate through internal gateway protocols in autonomous systems (AS). This paper makes a contribution in this field using (Graphical Network Simulator) GNS3 version 2.2.34 software to simulate a ten-router setup to mimic a complex real-time enterprise-level network. Every protocol is put into practice using the intended ***/Statistical Analysis: The interior gateway, Open Shortest Path First (OSPF), Enhanced Interior Gateway Routing Protocol (EIGRP), and Intermediate System to Intermediate System (IS-IS) are in-depth analyzed in a complex enterprise-level network, which made up to 30 multiple paths of routing data packets for each examined routing protocol, with consideration of link failure while employing various network ***: End-to-end delay, jitter, and convergence time for each protocol have been measured for comparison in a large-scale (a complex enterprise) fixed topology. Finding the best practical interior gateway routing protocol is the study's main goal. © 2023 IEEE.

关键词： Routing protocols

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Comparison of IoT Architectures Based on the Seven Essential Characteristics 3

Comparison of IoT Architectures Based on the Seven Essential...

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3rd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2023

作者： Al-Awami, Saleh Hussein Mahfud Al-Aty, Mohammed Al-Najar, Mousa Faraj University of Benghazi Department of Data Communications and Computer Networks Benghazi Libya

ISBN: (纸本)9798350319897

The Internet of Things (IoT) is expected to become one of the major technological hubs in the coming years by connecting smart physical objects and enabling a variety of applications to enable smart decision-making. This paper aims to identify and study a set of eight proposed architectures (a three-layers architecture, a five-layers architecture, SOA architecture, a 5G architecture, an Alibaba IoT architecture, an Oracle IoT architecture, and a novel IoT seven-layers architecture) for the Internet of Things. In addition, research challenges have been identified in the current trends of IoT architectures. The main contribution of this comparison paper is that it systematically reviews the current state of IoT architectures. The comparison according to the seven essential characteristics (simplicity, cost, scalability, flexibility, performance, security and privacy, and data analytic using machine learning) has been made to determine the best reference architecture that would provide an uniform basis to understand, compare, and evaluate the different solutions of IoT-based architectures. © 2023 IEEE.

关键词： Internet of things

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A Systematic Literature Review on Cybersecurity Risk Management in Smart Cities 6

A Systematic Literature Review on Cybersecurity Risk Managem...

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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Alshehri, Jawaher Alhamed, Almaha Rahman, M M Hafizur King Faisal University Department of Computer Networks and Communications College of Computer Science and Information Technology Al Hassa Al Hofuf31982 Saudi Arabia

ISBN: (纸本)9798350344349

A smart city is a digital and intelligent city that uses communication and information technology (ICT) that contains different types of technology methods to meet the needs of current and future generations regarding economic, social, cultural, and environmental aspects. Smart cities face many risks in technology, which make their management difficult and easy to attack. The concept of smart cities has emerged as a promising solution to face various urban challenges. In addition, it has been determined that a 38.7 percent increase in smart city risk management significantly increased the number of attacks across the TOE (technology, organization, and environment) categories. The research aims to highlight the role of cybersecurity risk management in smart cities, including preventing threats and the essential attacks that face smart cities. To strengthen the security and resilience of smart cities, it is crucial to comprehend the unique difficulties and dangers related to cybersecurity in these urban environments and take necessary steps accordingly. The primary objective of this research is to enhance the cybersecurity risk management role in smart cities, thereby guaranteeing the sustainable and secure growth of technologically sophisticated metropolitan regions. Index Terms-Cybersecurity risk management, smart cities, risk management, internet of things (IoT), artificial intelligence. © 2024 IEEE.

关键词： Risk management

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Automated Malware Detection Based on a Machine Learning Algorithm 10

Automated Malware Detection Based on a Machine Learning Algo...

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10th IEEE International Conference on communications and Networking, ComNet 2023

作者： Almuqren, Almaha Frikha, Mounir Albuali, Abdullah King Faisal University Department of Computer Networks and Communications Al Hofuf Saudi Arabia

ISBN: (纸本)9798350381719

Malware detection relies on the discriminative power of machine learning to identify new variants of malware samples. Automated malware detection, driven by machine learning algorithms, has garnered significant recognition for its capability to detect previously unknown malware. In recent years, various machine learning techniques have exhibited promise in enhancing malware detection. These techniques facilitate the analysis of substantial datasets, the identification of intricate patterns, and the detection of emergent threats, surpassing conventional signature-based methods. This paper offers an overview of these machine learning techniques and their potential to enhance the precision and efficiency of malware detection systems. The objective is to conduct a comprehensive literature review, analyze selected research papers, and present a taxonomy of machine learning methods for malware detection. The study delves into the intersection of malware and machine learning within the cybersecurity domain, encompassing the taxonomy of malware detection and the classification of machine learning algorithms. Moreover, the taxonomy is employed to evaluate the latest algorithms and perform an exhaustive analysis of machine learning approaches. Additionally, this paper discusses the challenges related to the application of machine learning in malware detection. Machine learning techniques provide a robust toolkit for the development of more effective and efficient malware detection systems. Continued research in this field is imperative to mitigate the ever-growing malware threat. © 2023 IEEE.

关键词： Taxonomies

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AMachine Learning Approach to Cyberbullying Detection in Arabic Tweets

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computers, Materials & Continua 2024年第7期80卷 1033-1054页

作者： Dhiaa Musleh Atta Rahman Mohammed Abbas Alkherallah Menhal Kamel Al-Bohassan Mustafa Mohammed Alawami Hayder Ali Alsebaa Jawad Ali Alnemer Ghazi Fayez Al-Mutairi May Issa Aldossary Dalal A.Aldowaihi Fahd Alhaidari Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Information Systems College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Networks and Communications College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia

With the rapid growth of internet usage,a new situation has been created that enables practicing *** has increased over the past decade,and it has the same adverse effects as face-to-face bullying,like anger,sadness,anxiety,and *** the anonymity people get on the internet,they tend to bemore aggressive and express their emotions freely without considering the effects,which can be a reason for the increase in cyberbullying and it is the main motive behind the current *** study presents a thorough background of cyberbullying and the techniques used to collect,preprocess,and analyze the ***,a comprehensive review of the literature has been conducted to figure out research gaps and effective techniques and practices in cyberbullying detection in various languages,and it was deduced that there is significant room for improvement in the Arabic *** a result,the current study focuses on the investigation of shortlisted machine learning algorithms in natural language processing(NLP)for the classification of Arabic datasets duly collected from Twitter(also known as X).In this regard,support vector machine(SVM),Naive Bayes(NB),Random Forest(RF),Logistic regression(LR),Bootstrap aggregating(Bagging),Gradient Boosting(GBoost),Light Gradient Boosting Machine(LightGBM),Adaptive Boosting(AdaBoost),and eXtreme Gradient Boosting(XGBoost)were shortlisted and investigated due to their effectiveness in the similar ***,the scheme was evaluated by well-known performance measures like accuracy,precision,Recall,and ***,XGBoost exhibited the best performance with 89.95%accuracy,which is promising compared to the state-of-the-art.

关键词： Supervised machine learning ensemble learning cyberbullying Arabic tweets NLP

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A Systematic Literature Review on Digital Forensic Investigation on Android Devices 2

A Systematic Literature Review on Digital Forensic Investiga...

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2nd International Conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Almuqren, Almaha Alsuwaelim, Hanan Hafizur Rahman, M.M. Ibrahim, Adamu A. Department of Computer Networks and Communications King Faisal University Saudi Arabia Saudi Arabia Department of Computer Science KICT International Islamic University Malaysia IIUM Kuala Lumpur Malaysia

Mobile forensics, particularly in the Android ecosystem, is a rapidly evolving field that demands continuous advancements to address the growing complexity and diversity of mobile devices. This article emphasizes the importance of developing techniques for digitally analyzing Android smartphones, which dominate the smartphone market. The primary objective of this research is to contribute to the development of effective forensic investigation strategies tailored specifically for Android mobile devices, providing insights into the tools and methods used for this purpose. The objective of this study is to improve the precision and effectiveness of forensic examinations pertaining to Android mobile phones. It discusses the fundamental functionality of mobile devices as a source of digital evidence and provides an overview of tools and methodologies for collecting and analyzing such evidence. The importance of comprehending the hardware and software architecture of Android handsets in order to choose the right forensic tools is also highlighted in the article. Furthermore, it proposes future enhancements for Andriller, a popular digital forensic tool, to improve its effectiveness in Android forensic investigations. These enhancements include advancements in data extraction techniques, compatibility with new Android versions, support for additional data types, integration with advanced analysis methods, and addressing identified limitations. Additionally, the paper stresses the need for robust methodologies for conducting cloud forensics on Android devices, particularly in the context of data stored in cloud storage services. The proposed work aims to enhance the capabilities of Andriller and improve the efficiency of digital forensic investigations on Android devices. © 2024 Elsevier B.V.. All rights reserved.

关键词： Android (operating system)

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A Robust Asynchrophasor in PMU Using Second-Order Kalman Filter

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computers, Materials & Continua 2023年第2期74卷 2557-2573页

作者： Nayef Alqahtani Ali Alqahtani Electrical and Computer Engineering Department School of Engineering and Computer ScienceOakland UniversityRochesterMI48326USA King Faisal University Al-Ahsa31982Kingdom of Saudi Arabia Department of Networks and Communications Engineering Najran UniversityNajran61441Kingdom of Saudi Arabia

Phasor Measurement Units(PMUs)provide Global Positioning System(GPS)time-stamped synchronized measurements of voltage and current with the phase angle of the system at certain points along with the grid *** synchronized data measurements are extracted in the form of amplitude and phase from various locations of the power grid to monitor and control the power system condition.A PMU device is a crucial part of the power equipment in terms of the cost and operative point of ***,such ongoing development and improvement to PMUs’principal work are essential to the network operators to enhance the grid quality and the operating *** paper introduces a proposed method that led to lowcost and less complex techniques to optimize the performance of PMU using Second-Order Kalman *** is based on the Asyncrhophasor technique resulting in a phase error minimization when receiving the signal from an access point or from the main access *** MATLAB model has been created to implement the proposed method in the presence of Gaussian and *** results have shown the proposed method which is Second-Order Kalman Filter outperforms the existing *** results were tested usingMean Square Error(MSE).The proposed Second-Order Kalman Filter method has been replaced with a synchronization unit into thePMUstructure to clarify the significance of the proposed new PMU.

关键词： Distributed generation asynchrophasor kalman filter phasor estimation phasor measurement unit state variables mean square error and signal to noise ratio

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Nanorobot-Based Intelligent Symptoms Analysis and Recommendation Framework in Edge networks

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IEEE Journal of Biomedical and Health Informatics 2023年 PP卷 1-8页

作者： Nandy, Sudarshan Hazra, Abhishek Adhikari, Mainak Puthal, Deepak School of Computer Science and Engineering SCOPE VIT University Vellore Tamil Nadu India Communications & Networks Lab National University of Singapore Singapore Department of Computer Science IIIT Lucknow Lucknow India Department of EECS Khalifa University UAE

Nanorobots are microscopic robots that operate at the molecular and cellular level and can potentially revolutionize fields such as medicine, manufacturing, and environmental monitoring due to their precision. However, the challenge for researchers is to analyze the data and provide a constructive recommendation framework instantly, as most nanorobots demand on-time and near-edge processing. To tackle this challenge, this research presents a novel edge-enabled intelligent data analytics framework called Transfer Learning Population Neural Network (TLPNN) to predict glucose levels and associated symptoms from invasive and non-invasive wearable devices. The TLPNN is designed to be unbiased in predicting symptoms during the initial phase but later modified based on the best-performing neural networks during the learning phase. The effectiveness of the proposed method is validated using two publicly available glucose datasets with various performance metrics. The simulation results demonstrate the effectiveness of the proposed TLPNN method over existing ones. IEEE

关键词： Neural networks

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Query Optimization Framework for Graph Database in Cloud Dew Environment

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computers, Materials & Continua 2023年第1期74卷 2317-2330页

作者： Tahir Alyas Ali Alzahrani Yazed Alsaawy Khalid Alissa Qaiser Abbas Nadia Tabassum Department of Computer Science Lahore Garrison UniversityLahorePakistan Faculty of Computer Science and Information Systems Islamic University Madinah Madinah42351Saudi Arabia Networks and Communications Department College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Science Virtual University of PakistanLahorePakistan

The query optimizer uses cost-based optimization to create an execution plan with the least cost,which also consumes the least amount of *** challenge of query optimization for relational database systems is a combinatorial optimization problem,which renders exhaustive search impossible as query sizes *** in CPU performance have surpassed main memory,and disk access speeds in recent decades,allowing data compression to be used—strategies for improving database performance *** performance enhancement,compression and query optimization are the two most *** reduces the volume of data,whereas query optimization minimizes execution *** the database reduces memory requirement,data takes less time to load into memory,fewer buffer missing occur,and the size of intermediate results is more *** paper performed query optimization on the graph database in a cloud dew environment by considering,which requires less time to execute a *** factors compression and query optimization improve the performance of the *** research compares the performance of MySQL and Neo4j databases in terms of memory usage and execution time running on cloud dew servers.

关键词： Query optimization compression cloud dew decompression graph database

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PAN-DeSpeck:A Lightweight Pyramid and Attention-Based Network for SAR Image Despeckling

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computers, Materials & Continua 2023年第9期76卷 3671-3689页

作者： Saima Yasmeen Muhammad Usman Yaseen Syed Sohaib Ali Moustafa M.Nasralla Sohaib Bin Altaf Khattak Department of Computer Sciences Comsats UniversityIslamabadPakistan Department of Research and Development Shearwater GeoservicesCrawleyWest SussexUK Department of Communications&Networks Engineering Prince Sultan UniversityRiyadhSaudi Arabia

SAR images commonly suffer fromspeckle noise,posing a significant challenge in their analysis and *** convolutional neural network(CNN)based despeckling methods have shown great performance in removing speckle ***,these CNN-basedmethods have a *** do not decouple complex background information in ***,they have deep network structures thatmay result in many parameters,limiting their applicability tomobile ***,extracting key speckle information in the presence of complex background is also a major problem with *** proposed study addresses these limitations by introducing a lightweight pyramid and attention-based despeckling(PAN-Despeck)*** primary objective is to enhance image quality and enable improved information interpretation,particularly on mobile devices and scenarios involving complex *** PAN-Despeck network leverages domainspecific knowledge and integrates Gaussian Laplacian image pyramid decomposition for multi-resolution image *** utilizing this approach,complex background information can be effectively decoupled,leading to enhanced despeckling ***,the attention mechanism selectively focuses on key speckle features and facilitates complex background *** network incorporates recursive and residual blocks to ensure computational efficiency and accelerate training speed,making it lightweight while maintaining high *** comprehensive evaluations,it is demonstrated that PAN-Despeck outperforms existing image restoration *** an impressive average peak signal-to-noise ratio(PSNR)of 28.355114 and a remarkable structural similarity index(SSIM)of 0.905467,it demonstrates exceptional performance in effectively reducing speckle noise in SAR *** source code for the PAN-DeSpeck network is available on GitHub.

关键词： Synthetic Aperture Radar(SAR) SAR image despeckling speckle noise deep learning pyramid networks multiscale image despeckling

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