[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122716-en":3,"doc-seo-122716-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122716,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Performance Evaluation of Optimized Predictive Model for Software Defined Network Traffic Management - Machine Learning","Communication across internet networks is fundamental for transferring data reliably and safely, yet increasing connectivity raises the likelihood of security breaches and cyber-attacks. This work builds an optimized predictive model for software defined network traffic management that estimates the ideal real-time path under dynamic SDN conditions and evolving threat landscapes. A robust, scalable system is developed using network infrastructure understanding, data analysis, and machine learning to generate route suggestions that reduce congestion. Nine ML algorithms are evaluated with percentage split, resampling, and cross validation, reaching 92.76% then improving to 98.40% after training, providing optimal routing with minimal congestion while supporting security and sustainability by lowering energy usage.","Performance Evaluation of Optimized Predictive Model for Software Defined Network Traffic Management using  \nMachine Learning  \nLokesh Pawar 1 , Gaurav Bathla2 , Rohit Bajaj3  \n1Ph.D Scholar, Computer Science & Engineering, Chandigarh University, India 2,3Faculty of Computer Science & Engineering Department, Chandigarh University, India  \nAbstract  \nCommunication channel is essential in any type of engagement for delivering and receiving  \ndata via the internet. To determine the most efficient and safe way through which network data  \nmay travel while minimizing the danger of network breaches or cyber-attacks. The objective is  \nto build an optimized network traffic management predictive model that can predict the ideal  \npath in real-time while accounting through the dynamic nature of software defined network  \ntraffic and the continuously changing danger of landscaping. To design a robust model of the  \ndata and scalable system that can suggest accurate suggestions of route to the network  \nmanagers, a thorough grasp of network’s infrastructure, data analysis, and machine learning  \ntechniques are applied. Choosing the optimum path route data from the sdn based network  \ntraffic dataset, the model suggests an optimal path to avoid network communication traffic and  \ncongestion. Here nine Machine Learning algorithms are explored and analysed their  \nperformance by using the percentage split, resampling and cross validation which originally  \nrecorded as 92.76% and after training with cross validation it improved to 98.40% providing  \nthe best optimal path with minimum congestions. Building the optimized network traffic  \nmanagement model not only provide network security but also contribute to environmental sustainability. Their capacity to properly filter and manage network traffic helps to decrease energy usage by predicting the optimal routes for software defined network traffic.  \n1First Author 2 Second Author  \n3Corresponding Author, email: [rohit.rick@gmail.com](rohit.rick@gmail.com)  \n© Common Ground Research Networks, Rohit Bajaj, All Rights Reserved. Acceptance: 25September2023, Publication: 26September2023  \nKeywords  \nCongestion, Breaches, Landscaping, Robust, Optimal Route, Percentage Split, Resampling, Cross-Validation, Environmental Sustainability, SDN.  \n1. Introduction  \nIn today's digital age, the use of the Internet has become a vital aspect of businesses and organizations of all sizes. However, with the increased usage of the internet [1], there is also a heightened risk of security breaches and cyber-attacks. To protect against these threats,  \norganizations use firewalls as a first line of defense to filter and control incoming and  \noutgoing network traffic. Ultimately, the purpose of this paper is to give useful insights into  \nthe optimization of predictive models for determining the optimum route based on internet  \nfirewall data, as well as the possible advantages for enterprises in maintaining a safe and  \nefficient network infrastructure. Communication is critical for any organization or business  \nseeking to succeed in today's data-driven environment [2,3] . Optimal route suggestions are  \nimportant when it comes to improving predictive models to guarantee that the model runs best  \nand gets the intended outcomes determining the optimum route based on internet firewall data  \nas illustrated in Figure 1. Secure communication is required in this environment to help data  \nscientists and business stakeholders comprehend the subtleties of the model, the data it  \nconsumes, and the outcomes it delivers. It is feasible to construct predictive models that are  \naccurate, efficient, and beneficial in supporting decision-making processes connected to  \ninternet routing by ensuring clear and straightforward communication amongst all parties  \ninvolved.  \nAs data flows over the network, logs are generated that may be studied to discover traffic  \npatterns and potential security issues. It is feasible to opti","cbCaioNRa97XNLkw","https://ap.wps.com/l/cbCaioNRa97XNLkw","pdf",624421,1,17,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n## Internet firewall data and predictive modeling\n## Network congestion and route optimization","[{\"question\":\"What problem does the paper address in SDN traffic management?\",\"answer\":\"It addresses the need to predict an ideal real-time route in software defined networks while minimizing security risks such as breaches and cyber-attacks and reducing congestion.\"},{\"question\":\"Which modeling approach and evaluation methods are used?\",\"answer\":\"The paper builds an optimized predictive model using machine learning and evaluates nine algorithms with percentage split, resampling, and cross validation.\"},{\"question\":\"What performance improvement is reported for the best model?\",\"answer\":\"The results are reported as 92.76% initially, improving to 98.40% after training with cross validation, achieving the best optimal path with minimum congestion.\"}]","Performance Evaluation of Optimized Predictive Model for Software Defined Network Traffic Management - 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