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Mobility-Aware Caching Strategy in an sdn-Based Cooperative Caching Network

中国通信:英文版 2023年第9期20卷 196-214页

作者： Qi Li Xiaoxiang Wang Dongyu Wang Information and Communication Engineering Beijing University of TechnologyBeijing 100124China Beijing University of Posts and Telecommunications Beijing 100876China

The upsurge of mobile multimedia traf-fic puts a heavy burden on the cellular network,and wireless caching has emerged as a powerful tech-nique to overcome the backhaul bottleneck and al-leviate the network burden.However,most previ-ous works ignored user mobility,thus not reaping the caching gain from user mobility and having lim-ited practical applications.In this paper,a mobility-aware caching strategy for the software-defined net-work(sdn)-based network is studied.Firstly,since typical mobile user(MU)has multiple opportunities to connect with the nearby MUs and Small-base stations(SBSs),the contact times between MUs as well as be-tween MU and SBSs are derived as the Poisson distri-bution and Gamma distribution.Secondly,we propose a two-tier cooperative caching strategy,where SBSs cache the rateless Fountain code encoded video blocks probabilistically and nonrepeatedly while MUs just store the whole encoded video received last time.The corresponding four-stage transmission process is ana-lyzed,where the key intermediate step is the deriva-tion of the service and the failing probabilities of each transmission manner.Finally,we derive the success-ful offloading rate and average data offloading ra-tio(ADOR)as performance metrics.A system op-timization problem based on ADOR is formulated,and two solutions are proposed,namely,derivativebased solution(DB-Solution)and long-tail distribution approximation(LTD-Approximation).Simulation results demonstrate that the effectiveness of LTDApproximation is similar to the DB-Solution,and the proposed caching strategy can achieve quasi-optimal performance compared with other contrast schemes.

关键词： wireless caching mobility sdn

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WiMA:Towards a Multi-Criterion Association in Software Defined Wi-Fi Networks

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Computers, Materials & Continua 2023年第5期75卷 2347-2363页

作者： Sohaib Manzoor Hira Manzoor Saddaf Rubab Muhammad Attique Khan Majed Alhaisoni Abdullah Alqahtani Ye Jin Kim Byoungchol Chang Department of Electrical Engineering Mirpur University of Science and Technology(MUST)Mirpur-10250(AJK)Pakistan Faculty of Electronics Telecommunications and InformaticsGdansk University of Technology80-233 GdanskPoland Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates National University of Sciences and Technology(NUST) Islamabad44000Pakistan Department of Computer Science HITEC UniversityTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

Despite the planned installation and operations of the traditional IEEE 802.11 networks,they still experience degraded performance due to the number of inefficiencies.One of the main reasons is the received signal strength indicator(RSSI)association problem,in which the user remains connected to the access point(AP)unless the RSSI becomes too weak.In this paper,we propose a multi-criterion association(WiMA)scheme based on software defined networking(sdn)in Wi-Fi networks.An association solution based on multi-criterion such as AP load,RSSI,and channel occupancy is proposed to satisfy the quality of service(QoS).sdnhaving an overall view of the network takes the association and reassociation decisions making the handoffs smooth in throughput performance.To implementWiMA extensive simulations runs are carried out on Mininet-NS3-Wi-Fi network simulator.The performance evaluation shows that the WiMA significantly reduces the average number of retransmissions by 5%–30%and enhances the throughput by 20%–50%,hence maintaining user fairness and accommodating more wireless devices and traffic load in the network,when compared to traditional client-driven(CD)approach and state of the art Wi-Balance approach.

关键词： Association multi-criterion sdn Wi-Fi

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Effectively Handling Network Congestion and Load Balancing in Software-Defined Networking

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Computers, Materials & Continua 2022年第1期70卷 1363-1379页

作者： Shabir Ahmad Faisal Jamil Abid Ali Ehtisham Khan Muhammad Ibrahim Taeg Keun Whangbo Department of I.T.Convergence Engineering Gachon UniversitySujeong-GuSeongnam-Si461-701Gyeonggi-DoKorea Department of Computer Engineering Jeju National UniversityJeju-Si63243JejuKorea University of Engineering and Technology Taxila47080Pakistan The University of Haripur Haripur22620Pakistan

The concept of Software-Defined Networking(sdn)evolves to overcome the drawbacks of the traditional networks with Internet Protocol(I.P.)packets sending and packets handling.The sdn structure is one of the critical advantages of efficiently separating the data plane from the control plane tomanage the network configurations and network management.Whenever there aremultiple sending devices inside the sdnnetwork,theOpenFlow switches are programmed to handle the limited number of requests for their interface.When the recommendations are exceeded from the specific threshold,the load on the switches also increases.This research article introduces a new approach named LBoBS to handle load balancing by adding the load balancing server to the sdn network.Besides,it is used to maximize sdn’s reliability and efficiency.It also works in coordination with the controller to effectively handle the load balancing policies.The load balancing server is implemented to manage the switches load effectively.Results are evaluated on the NS-3 simulator for packet delivery,bandwidth utilization,latency control,and packet decision ratios on the OpenFlow switches.It has been found that the proposed method improved sdn’s load balancing by 70%compared to the previous state-of-the-art methods.

关键词： sdn control plane load balancing decision tree CPU utilization

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Optimized Load Balancing Technique for Software Defined Network

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Computers, Materials & Continua 2022年第7期72卷 1409-1426页

作者： Aashish Kumar Darpan Anand Sudan Jha Gyanendra Prasad Joshi Woong Cho Department of Computer Science and Engineering Chandigarh UniversityMohaliPunjab140413India School of Computational Sciences CHRIST(Deemed to be University)Ghaziabad201003India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Automotive ICT Convergence Engineering Daegu Catholic UniversityGyeongsan38430Korea

Software-defined networking is one of the progressive and prominent innovations in Information and Communications Technology.It mitigates the issues that our conventional network was experiencing.However,traffic data generated by various applications is increasing day by day.In addition,as an organization’s digital transformation is accelerated,the amount of information to be processed inside the organization has increased explosively.It might be possible that a Software-Defined Network becomes a bottleneck and unavailable.Various models have been proposed in the literature to balance the load.However,most of the works consider only limited parameters and do not consider controller and transmission media loads.These loads also contribute to decreasing the performance of Software-Defined Networks.This work illustrates how a software-defined network can tackle the load at its software layer and give excellent results to distribute the load.We proposed a deep learning-dependent convolutional neural networkbased load balancing technique to handle a software-defined network load.The simulation results show that the proposed model requires fewer resources as compared to existing machine learning-based load balancing techniques.

关键词： sdn software-defined networks load balancing performance enhancement

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Probe Attack Detection Using an Improved Intrusion Detection System

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Computers, Materials & Continua 2023年第3期74卷 4769-4784页

作者： Abdulaziz Almazyad Laila Halman Alaa Alsaeed Department of Computer Engineering College of Computer ScienceKing Saud UniversityRiyadh11421Saudi Arabia

The novel SoftwareDefined Networking(sdn)architecture potentially resolves specific challenges arising from rapid internet growth of and the static nature of conventional networks to manage organizational business requirements with distinctive features.Nevertheless,such benefits lead to a more adverse environment entailing network breakdown,systems paralysis,and online banking fraudulence and robbery.As one of the most common and dangerous threats in sdn,probe attack occurs when the attacker scans sdn devices to collect the necessary knowledge on system susceptibilities,which is thenmanipulated to undermine the entire system.Precision,high performance,and real-time systems prove pivotal in successful goal attainment through feature selection to minimize computation time,optimize prediction performance,and provide a holistic understanding of machine learning data.As the extension of astute machine learning algorithms into an Intrusion Detection System(IDS)through sdn has garnered much scholarly attention within the past decade,this study recommended an effective IDS under the Grey-wolf optimizer(GWO)and Light Gradient Boosting Machine(Light-GBM)classifier for probe attack identification.The Insdn dataset was employed to train and test the proposed IDS,which is deemed to be a novel benchmarking dataset in sdn.The proposed IDS assessment demonstrated an optimized performance against that of peer IDSs in probe attack detection within sdn.The results revealed that the proposed IDS outperforms the state-of-the-art IDSs,as it achieved 99.8%accuracy,99.7%recall,99.99%precision,and 99.8%F-measure.

关键词： GWO IDS Insdn LightGBM probe attack sdn

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An Efficient Intrusion Detection Framework in Software-Defined Networking for Cybersecurity Applications

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Computers, Materials & Continua 2022年第8期72卷 3529-3548页

作者： Ghalib H.Alshammri Amani K.Samha Ezz El-Din Hemdan Mohammed Amoon Walid El-Shafai Department of Computer Science Community CollegeKing Saud UniversityRiyadh28095Saudi Arabia Deanship of Scientific Research Saudi Electronic UniversityRiyadhSaudi Arabia Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh28095Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Electronics and Electrical Communications Eng.Depart. Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Network management and multimedia data mining techniques have a great interest in analyzing and improving the network traffic process.In recent times,the most complex task in Software Defined Network(sdn)is security,which is based on a centralized,programmable controller.Therefore,monitoring network traffic is significant for identifying and revealing intrusion abnormalities in the sdn environment.Consequently,this paper provides an extensive analysis and investigation of the NSL-KDD dataset using five different clustering algorithms:K-means,Farthest First,Canopy,Density-based algorithm,and Exception-maximization(EM),using the Waikato Environment for Knowledge Analysis(WEKA)software to compare extensively between these five algorithms.Furthermore,this paper presents an sdn-based intrusion detection system using a deep learning(DL)model with the KDD(Knowledge Discovery in Databases)dataset.First,the utilized dataset is clustered into normal and four major attack categories via the clustering process.Then,a deep learning method is projected for building an efficient sdn-based intrusion detection system.The results provide a comprehensive analysis and a flawless reasonable study of different kinds of attacks incorporated in the KDD dataset.Similarly,the outcomes reveal that the proposed deep learning method provides efficient intrusion detection performance compared to existing techniques.For example,the proposed method achieves a detection accuracy of 94.21%for the examined dataset.

关键词： Deep neural network DL WEKA network traffic intrusion and anomaly detection sdn clustering and classification KDD dataset

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RMCARTAM For DDoS Attack Mitigation in sdn Using Machine Learning

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Computer Systems Science & Engineering 2023年第6期45卷 3023-3036页

作者： M.Revathi V.V.Ramalingam B.Amutha Department of Computing Technologies SRM Institute of Science and TechnologyKattankulathurChennai603203Tamil NaduIndia

The impact of a Distributed Denial of Service(DDoS)attack on Soft-ware Defined Networks(sdn)is briefly analyzed.Many approaches to detecting DDoS attacks exist,varying on the feature being considered and the method used.Still,the methods have a deficiency in the performance of detecting DDoS attacks and mitigating them.To improve the performance of sdn,an efficient Real-time Multi-Constrained Adaptive Replication and Traffic Approximation Model(RMCARTAM)is sketched in this article.The RMCARTAM considers different parameters or constraints in running different controllers responsible for handling incoming packets.The model is designed with multiple controllers to handle net-work traffic but can turn the controllers according to requirements.The multi-con-straint adaptive replication model monitors different features of network traffic like rate of packet reception,class-based packet reception and target-specific reception.According to these features,the method estimates the Replication Turn-ing Weight(RTW)based on which triggering controllers are performed.Similarly,the method applies Traffic Approximation(TA)in the detection of DDoS attacks.The detection of a DDoS attack is performed by approximating the incoming traf-fic to any service and using various features like hop count,payload,service fre-quency,and malformed frequency to compute various support measures on bandwidth access,data support,frequency support,malformed support,route sup-port,and so on.Using all these support measures,the method computes the value of legitimate weight to conclude the behavior of any source in identifying the mal-icious node.Identified node details are used in the mitigation of DDoS attacks.The method stimulates the network performance by reducing the power factor by switching the controller according to different factors,which also reduces the cost.In the same way,the proposed model improves the accuracy of detecting DDoS attacks by estimating the features of incoming traffic in different corners.

关键词： DDoS sdn traffic approximation adaptive replication multi-controller support measures RTW

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An Intent-Driven Closed-Loop Platform for 5G Network Service Orchestration

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Computers, Materials & Continua 2022年第3期70卷 4323-4340页

作者： Talha Ahmed Khan Khizar Abbas Afaq Muhammad Wang-Cheol Song Department of Computer Engineering Jeju National UniversityJejuKorea

The scope of the 5G network is not only limited to the enhancements in the form of the quality of service(QoS),but it also includes a wide range of services with various requirements.Besides this,many approaches and platforms are under the umbrella of 5G to achieve the goals of endto-end service provisioning.However,the management of multiple services over heterogeneous platforms is a complex task.Each platform and service have various requirements to be handled by domain experts.Still,if the next-generation network management is dependent on manual updates,it will become impossible to provide seamless service provisioning in runtime.Since the traffic for a particular type of service varies significantly over time,automatic provisioning of resources and orchestration in runtime need to be integrated.Besides,with the increase in the number of devices,amount,and variety of traffic,the management of resources with optimization becomes a challenging task.To this end,this manuscript provides a solution that automates the management and service provisioning through multiple platforms while assuring various aspects,including automation,resource management and service assurance.The solution consists of an intent-based system that automaticallymanages different orchestrators,and eliminates manual control by abstracting the complex configuration requirements into simple and generic contracts.The proposed systemconsiders handling the scalability of resources in runtime by usingMachine Learning(ML)to automate and optimize service resource utilization.

关键词： IBN ML 5G NFV XOS OSM sdn ONOS OpenStack

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Anomaly Detection and Access Control for Cloud-Edge Collaboration Networks

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Intelligent Automation & Soft Computing 2023年第8期37卷 2335-2353页

作者： Bingcheng Jiang Qian He Zhongyi Zhai Hang Su College of Computers and Information Security Guilin University of Electronic TechnologyGuilin541004China

Software-defined networking(sdn)enables the separation of control and data planes,allowing for centralized control and management of the network.Without adequate access control methods,the risk of unau-thorized access to the network and its resources increases significantly.This can result in various security breaches.In addition,if authorized devices are attacked or controlled by hackers,they may turn into malicious devices,which can cause severe damage to the network if their abnormal behaviour goes undetected and their access privileges are not promptly restricted.To solve those problems,an anomaly detection and access control mechanism based on sdn and neural networks is proposed for cloud-edge collaboration networks.The system employs the Attribute Based Access Control(ABAC)model and smart contract for fine-grained control of device access to the network.Furthermore,a cloud-edge collaborative Key Performance Indicator(KPI)anomaly detection method based on the Gated Recurrent Unit and Generative Adversarial Nets(GRU-GAN)is designed to discover the anomaly devices.An access restriction mechanism based on reputation value and anomaly detection is given to prevent anomalous devices.Experiments show that the proposed mechanism performs better anomaly detection on several datasets.The reputation-based access restriction effectively reduces the number of malicious device attacks.

关键词： Cloud-edge sdn anomaly detection GRU-GAN

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Managing Smart Technologies with Software-Defined Networks for Routing and Security Challenges: A Survey

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Computer Systems Science & Engineering 2023年第11期47卷 1839-1879页

作者： Babangida Isyaku Kamalrulnizam Bin Abu Bakar Department of Computer Science Faculty of ComputingUniversiti Teknologi MalaysiaJohor BahruJohor81310Malaysia Department of Computer Science Faculty of Computing and Information TechnologySule Lamido UniversityP.M.B.048Jigawa StateNigeria

Smart environments offer various services,including smart cities,ehealthcare,transportation,and wearable devices,generating multiple traffic flows with different Quality of Service(QoS)demands.Achieving the desired QoS with security in this heterogeneous environment can be challenging due to traffic flows and device management,unoptimized routing with resource awareness,and security threats.Software Defined Networks(sdn)can help manage these devices through centralized sdn controllers and address these challenges.Various schemes have been proposed to integrate sdn with emerging technologies for better resource utilization and security.Software Defined Wireless Body Area Networks(SDWBAN)and Software Defined Internet of Things(SDIoT)are the recently introduced frameworks to overcome these challenges.This study surveys the existing SDWBAN and SDIoT routing and security challenges.The paper discusses each solution in detail and analyses its weaknesses.It covers SDWBAN frameworks for efficient management of WBAN networks,management of IoT devices,and proposed security mechanisms for IoT and data security in WBAN.The survey provides insights into the state-of-the-art in SDWBAN and SDIoT routing with resource awareness and security threats.Finally,this study highlights potential areas for future research.

关键词： sdn WBAN IoT routing security

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