[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119064-en":3,"doc-seo-119064-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},119064,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Insights of machine learning-based threat identification schemes in advanced network system","An advanced network system (ANS) supports collaborative communication patterns that enable modern applications, but security remains a growing deployment concern. Machine learning is widely viewed as a cost-effective way to strengthen defenses and manage ongoing security problems, yet its overall effectiveness scale is not clearly established. This paper provides a systematic review of existing machine learning variants for threat identification in ANS and examines how these approaches affect practical use cases, supported by gap analysis and learning outcomes.","Insights of machine learning-based threat identification schemes  \nin advanced network system  \nThanuja Narasimhamurthy, Gunavathi Hosahalli Swamy  \nDepartment of Computer Science and Engineering, Bangalore Institute of Technology, Bengaluru, Affiliated to Visvesvaraya  \nTechnological University, Belagavi, India  \nArticle Info ABSTRACT  \nArticle history:  \nReceived Aug 12, 2023 Revised May 8, 2024 Accepted May 12, 2024  \nKeywords:  \nAdvanced network system Collaborative  \nMachine learning Security  \nThreat identification  \nCorresponding Author:  \nAn advanced network system (ANS) is characterized by extensive communication features that can support a sophisticated collaborative network structure. This is essential to hosting various forms of upcoming modernized and innovative applications. Security is one of the rising concerns associated with ANS deployment. It is also noted that machine learning is one of the preferred cost-effective ways to optimize the security strength and address various ongoing security problems in ANS; however, it is still unknown about its overall effectivity scale. Hence, this paper contributes to a systematic review of existing variants of machine learning approaches to deal with threat identification in ANS. As ANS is a generalized form, this discussion considers the impact of existing machine learning approaches on its practical use cases. The paper also contributes towards critical gap analysis and highlights the study's potential learning outcome.  \nThis is an open access article under the CC BY-SA license.  \nThanuja Narasimhamurthy  \nDepartment of Computer Science and Engineering, Bangalore Institute of Technology, Bengaluru, Affiliated to Visvesvaraya Technological University  \nBelagavi, Karnataka, India  \n[Email: nthanuja@bit-bangalore.edu.in](Email: nthanuja@bit-bangalore.edu.in)  \n1. INTRODUCTION  \nWith the evolving new demands of applications and enterprises, the network and communication system area has consistently changed to meet the demands [1]–[3] . This led to the evolution of the advanced networking system (ANS), which is meant to improve communication services in manifold aspects [4] significantly. Adoption of advanced networking systems leads to better connectivity and communication among devices, organizations, and individuals, thereby facilitating reliable transmission of data with speedy networks where instantaneous information sharing is facilitated [5] . Another significant role of ANS is towards internet of things (IoT), where a vast array of devices is connected to facilitate aggregation and analysis of data as well as automation [6] . With increasing futuristic planning towards modernized and innovative transportation systems, industries, and smart cities, IoT is gaining its adoption in the commercial market faster. The importance ofANS can also be realized in its deployment area of cloud computing, where many sophisticated operations can be relayed to users hosted on a cloud platform using varied services [7] . Additionally, ANS facilitates cloud computing towards forming a collaborative network with higher scalability and high-speed networks. It is also noted that the importance of big data is gaining in the current era. It can provide data storage and mapping, transmission, and efficient collection, followed by applying a wide range of analytics to derive meaningful information [8] . The applicability of big data analytics contributes towards ANS in the perspective of data exchange supportability that can be used in scientific  \nresearch, healthcare, and business intelligence. ANS also improves business operations by offering innovative mechanisms to share resources within an organization, forming a customized collaborative network, and offering seamless communications [9] . With more preference towards virtual meetings, ANS can offer a collaborative platform that supports real-time streaming, sharing of necessary files, and interactive conferences, minimizing geographic","cbCaimFaZmb6cqb0","https://ap.wps.com/l/cbCaimFaZmb6cqb0","pdf",313458,1,11,"English","en",105,"# Introduction\n## Motivation and role of advanced network systems\n## Security challenges in advanced network systems\n## Paper contributions and review direction","[{\"question\":\"What is the focus of the paper?\",\"answer\":\"The paper systematically reviews existing machine learning approaches for threat identification in advanced network systems and assesses their practical impact.\"},{\"question\":\"Why is security important in advanced network systems?\",\"answer\":\"ANS supports extensive connectivity and collaborative communications for modern applications, but it also increases exposure to cyber threats such as unauthorized access, DoS attacks, data breaches, and malware.\"},{\"question\":\"What gaps does the paper aim to address?\",\"answer\":\"The paper highlights the unclear effectiveness scale of machine learning for ANS threat identification and performs critical gap analysis, outlining potential learning outcomes.\"}]","Insights of machine learning-based threat identification schemes in advanced network system | 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is the focus of the paper?","Question",{"text":75,"@type":76},"The paper systematically reviews existing machine learning approaches for threat identification in advanced network systems and assesses their practical impact.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is security important in advanced network systems?",{"text":80,"@type":76},"ANS supports extensive connectivity and collaborative communications for modern applications, but it also increases exposure to cyber threats such as unauthorized access, DoS attacks, data breaches, and malware.",{"name":82,"@type":73,"acceptedAnswer":83},"What gaps does the paper aim to address?",{"text":84,"@type":76},"The paper highlights the unclear effectiveness scale of machine learning for ANS threat identification and performs critical gap analysis, outlining potential learning 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