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Computer networks and internets 2nd ed.

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Component Importance and Interdependence Analysis for Transmission, Distribution and Communication Systems

1999年

作者： Douglas E. Comer.

来源：畅想之星图书光盘版（南... 评论

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CSEE Journal of Power and Energy Systems 2022年第2期8卷 488-498页

作者： Tao Fu Dexin Wang Xiaoyuan Fan Qiuhua Huang Pacific Northwest National Laboratory Washington 99354United States

For critical infrastructure restoration planning,the real-time scheduling and coordination of system restoration efforts,the key in decision-making is to prioritize those critical components that are out of service during the restoration.For this purpose,there is a need for component importance analysis.While it has been investigated extensively for individual systems,component importance considering interdependence among transmission,distribution and communication(T&D&C)systems has not been systematically analyzed and widely adopted.In this study,we propose a component importance assessment method in the context of interdependence between T&D&C networks.Analytic methods for multilayer networks and a set of metrics have been applied for assessing the component importance and interdependence between T&D&C networks based on their physical characteristics.The proposed methodology is further validated with integrated synthetic Illinois regional transmission,distribution,and communication(T&D&C)systems,the results reveal the unique characteristics of component/node importance,which may be strongly affected by the network topology and cross-domain node mapping.

关键词： Multilayer network interdependence Computer networks

An Optimal Scheme for WSN Based on Compressed Sensing

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computers, Materials & Continua 2022年第7期72卷 1053-1069页

作者： Firas Ibrahim AlZobi Ahmad Ali AlZubi Kulakov Yurii Abdullah Alharbi Jazem Mutared Alanazi Sami Smadi Department of Information Systems and networks Faculty of Information TechnologyThe World Islamic Sciences&Education UniversityJordan computer Science Department Community CollegeKing Saud UniversitySaudi Arabia Department of computer Engineering National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic InstituteUkraine

Wireless sensor networks(WSNs)is one of the renowned ad hoc network technology that has vast varieties of applications such as in Computer networks,bio-medical engineering,agriculture,industry and many more.It has been used in the internet-of-things(IoTs)applications.A method for data collecting utilizing hybrid compressive sensing(CS)is developed in order to reduce the quantity of data transmission in the clustered sensor network and balance the network load.Candidate cluster head nodes are chosen first from each temporary cluster that is closest to the cluster centroid of the nodes,and then the cluster heads are selected in order based on the distance between the determined cluster head node and the undetermined candidate cluster head node.Then,each ordinary node joins the cluster that is nearest to it.The greedy CS is used to compress data transmission for nodes whose data transmission volume is greater than the threshold in a data transmission tree with the Sink node as the root node and linking all cluster head nodes.The simulation results demonstrate that when the compression ratio is set to ten,the data transfer volume is reduced by a factor of ten.When compared to clustering and SPT without CS,it is reduced by 75%and 65%,respectively.When compared to SPT with Hybrid CS and Clustering with hybrid CS,it is reduced by 35%and 20%,respectively.Clustering and SPT without CS are compared in terms of node data transfer volume standard deviation.SPT with Hybrid CS and clustering with Hybrid CS were both reduced by 62%and 80%,respectively.When compared to SPT with hybrid CS and clustering with hybrid CS,the latter two were reduced by 41%and 19%,respectively.

关键词： Compressed sensing Computer networks sensor networks ad hoc networks

An Optimal Framework for SDN Based on Deep Neural Network

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computers, Materials & Continua 2022年第10期73卷 1125-1140页

作者： Abdallah Abdallah Mohamad Khairi Ishak Nor Samsiah Sani Imran Khan Fahad RAlbogamy Hirofumi Amano Samih M.Mostafa Department of Industrial Engineering School of Applied Technical Sciences German Jordanian UniversityAmman35247Jordan School of Electrical and Electronic Engineering Universiti Sains MalaysiaNibong Tebal14300Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and TechnologyThe National University of Malaysia(UKM)Bangi43600SelangorMalaysia Department of Electrical Engineering University of Engineering and TechnologyPeshawar814Pakistan Turabah University College Computer Sciences ProgramTaif UniversityTaif21944Saudi Arabia Research Institute for Information Technology Kyushu UniversityFukuoka819-0395Japan computer Science-Mathematics Department Faculty of ScienceSouth Valley UniversityQena83523Egypt

Software-defined networking(SDN)is a new paradigm that promises to change by breaking vertical integration,decoupling network control logic from the underlying routers and switches,promoting(logical)network control centralization,and introducing network programming.However,the controller is similarly vulnerable to a“single point of failure”,an attacker can execute a distributed denial of service(DDoS)attack that invalidates the controller and compromises the network security in SDN.To address the problem of DDoS traffic detection in SDN,a novel detection approach based on information entropy and deep neural network(DNN)is proposed.This approach contains a DNN-based DDoS traffic detection module and an information-based entropy initial inspection module.The initial inspection module detects the suspicious network traffic by computing the information entropy value of the data packet’s source and destination Internet Protocol(IP)addresses,and then identifies it using the DDoS detection module based on DNN.DDoS assaults were found when suspected irregular traffic was validated.Experiments reveal that the algorithm recognizes DDoS activity at a rate of more than 99%,with a much better accuracy rate.The false alarm rate(FAR)is much lower than that of the information entropy-based detection method.Simultaneously,the proposed framework can shorten the detection time and improve the resource utilization efficiency.

关键词： Deep neural network Computer networks data security optimization

Enhanced Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection

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computer Systems Science & Engineering 2023年第6期45卷 3037-3052页

作者： Fatma S.Alrayes Najm Alotaibi Jaber S.Alzahrani Sana Alazwari Areej Alhogail Ali M.Al-Sharafi Mahmoud Othman Manar Ahmed Hamza Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Prince Saud Al Faisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversitySaudi Arabia Department of computer Science College of Computers and Information TechnologyUniversity of BishaSaudi Arabia Department of computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Recent developments in Computer networks and Internet of Things(IoT)have enabled easy access to data.But the government and business sectors face several difficulties in resolving cybersecurity network issues,like novel attacks,hackers,internet criminals,and so on.Presently,malware attacks and software piracy pose serious risks in compromising the security of IoT.They can steal confidential data which results infinancial and reputational losses.The advent of machine learning(ML)and deep learning(DL)models has been employed to accomplish security in the IoT cloud environment.This article pre-sents an Enhanced Artificial Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection(EAGTODL-CTD)in IoT Cloud Net-works.The presented EAGTODL-CTD model encompasses the identification of the threats in the IoT cloud environment.The proposed EAGTODL-CTD mod-el mainly focuses on the conversion of input binaryfiles to color images,where the malware can be detected using an image classification problem.The EAG-TODL-CTD model pre-processes the input data to transform to a compatible for-mat.For threat detection and classification,cascaded gated recurrent unit(CGRU)model is exploited to determine class labels.Finally,EAGTO approach is employed as a hyperparameter optimizer to tune the CGRU parameters,showing the novelty of our work.The performance evaluation of the EAGTODL-CTD model is assessed on a dataset comprising two class labels namely malignant and benign.The experimental values reported the supremacy of the EAG-TODL-CTD model with increased accuracy of 99.47%.

关键词： Cybersecurity Computer networks threat detection internet of things cloud computing deep learning

Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection

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computer Systems Science & Engineering 2023年第9期46卷 3103-3119页

作者： Khaled M.Alalayah Fatma S.Alrayes Mohamed K.Nour Khadija M.Alaidarous Ibrahim M.Alwayle Heba Mohsen Ibrahim Abdulrab Ahmed Mesfer Al Duhayyim Department of computer Science College of Science and ArtsNajran UniversitySharurahSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

Malware is a‘malicious software program that performs multiple cyberattacks on the Internet,involving fraud,scams,nation-state cyberwar,and cybercrime.Such malicious software programs come under different classifications,namely Trojans,viruses,spyware,worms,ransomware,Rootkit,botnet malware,etc.Ransomware is a kind of malware that holds the victim’s data hostage by encrypting the information on the user’s computer to make it inaccessible to users and only decrypting it;then,the user pays a ransom procedure of a sum of money.To prevent detection,various forms of ransomware utilize more than one mechanism in their attack flow in conjunction with Machine Learning(ML)algorithm.This study focuses on designing a Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection(LBAAA-OMLMD)approach in Computer networks.The presented LBAAA-OMLMDmodelmainly aims to detect and classify the existence of ransomware and goodware in the network.To accomplish this,the LBAAA-OMLMD model initially derives a Learning-Based Artificial Algae Algorithm based Feature Selection(LBAAA-FS)model to reduce the curse of dimensionality problems.Besides,the Flower Pollination Algorithm(FPA)with Echo State Network(ESN)Classification model is applied.The FPA model helps to appropriately adjust the parameters related to the ESN model to accomplish enhanced classifier results.The experimental validation of the LBAAA-OMLMD model is tested using a benchmark dataset,and the outcomes are inspected in distinct measures.The comprehensive comparative examination demonstrated the betterment of the LBAAAOMLMD model over recent algorithms.

关键词： Computer networks machine learning security malware detection feature selection ransomware

Wireless Sensor Security Issues on Data Link Layer:A Survey

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computers, Materials & Continua 2023年第5期75卷 4065-4084页

作者： Muhammad Zulkifl Hasan Zurina Mohd Hanapi Muhammad Zunnurain Hussain Department of Communication Technology and Network Faculty of Computer Science and Information TechnologyUniversiti Putra MalaysiaSerdangSelangor43400Malaysia

A computer network can be defined as many computing devices connected via a communication medium like the internet.computer network development has proposed how humans and devices communicate today.These networks have improved,facilitated,and made conventional forms of communication easier.However,it has also led to uptick in-network threats and assaults.In 2022,the global market for information technology is expected to reach$170.4 billion.However,in contrast,95%of cyber security threats globally are caused by human action.These networks may be utilized in several control systems,such as home-automation,chemical and physical assault detection,intrusion detection,and environmental monitoring.The proposed literature review presents a wide range of information on Wireless Social networks(WSNs)and Internet of Things(IoT)frameworks.The aim is first to be aware of the existing issues(issues with traditional methods)and network attacks on WSN and IoT systems and how to defend them.The second is to review the novel work in the domain and find its limitations.The goal is to identify the area’s primary gray field or current research divide to enable others to address the range.Finally,we concluded that configuration.Message Rapid Spanning Tree Protocol(RSTP)messages have higher efficiency in network performance degradation than alternative Bridge Data Unit Protocol(BPDU)forms.The research divides our future research into solutions and newly developed techniques that can assist in completing the lacking component.In this research,we have selected articles from 2015 to 2021 to provide users with a comprehensive literature overview.

关键词： Wireless sensor networks(WSN) internet of things(IoT) industrial revolution 4.0(IR4.0) Computer networks network security

Effects of Intrinsic Decoherence on Information Transport in a Spin Chain

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Chinese Physics Letters 2009年第2期26卷 53-56页

作者：曾天海邵彬邹健 Department of Physics Beijing Institute of Technology Beijing 100081

Considering Milburn＇s intrinsic decoherence effect on quantum communication through a spin chain, we show that the transfer quality for quantum state and entanglement will obviously decrease with the increasing intrinsic decoherence rate. Some odd chains are much higher than even ones for the state transfer efficiency. The state transfer of a long chain is very sensitive to the intrinsic decoherence, which turns out to be an obstacle for information transport.

关键词： COHERENT states QUANTUM communication DATA transmission systems INFORMATION storage & retrieval systems INFORMATION networks Computer networks

175-W continuous-wave master oscillator power amplifier structure ytterbium-doped all-fiber laser

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Chinese Optics Letters 2008年第8期6卷 580-582页

作者：闫平殷树鹏巩马理 Center for Photonics and Electronics State Key Laboratory of TribologyDepartment of Precision InstrumentsTsinghua University

We report on hundred watts range ytterbium-doped all-fiber laser assembly based on the master oscillator power amplifier structure. It consisted of an oscillator and an amplifier with all-fiber components. And fiber fusion splice made the laser be an integrated fiber system. It generated up to 175.5 W of continuous- wave （CW） output power at 1085 nm with more than 75% extraction efficiency in the amplifier when the total coupled pump power into the double clad fiber was 270 W.

关键词： Architectural design Broadband amplifiers Computer networks Extraction Lasers Power amplifiers Ytterbium