[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126818-en":3,"doc-seo-126818-105":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},126818,1099523885074,"Ivy","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Multi-Layer Multi-Technology Firewall Optimisation in Beyond 5G Networks Using Machine Learning Classifiers","Enhancing security for Beyond 5G and Pre-6G networks is challenging, especially when implementing firewalls across diverse technologies with template-based rules that ignore real-time network status, leading to sub-optimal configurations. This paper presents an architecture to optimize multi-layer, multi-technology firewalls within a Beyond 5G network testbed, combining monitoring with automatic rule deployment into iptables, Open vSwitch, and Linux traffic control. Using four ML models for optimal selection, Random Forest achieves the strongest performance with an F1-score of 0.9083.","“© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”  \nMulti-Layer Multi-Technology Firewall Optimisation in Beyond 5G Networks Using Machine Learning  \nClassifiers  \n1st Jimena Andrade-Hoz University of the West of Scotland Paisley, United Kingdom [jimena.andrade-hoz@uws.ac.uk](jimena.andrade-hoz@uws.ac.uk)  \n2nd Jose M. Alcaraz-Calero  \nUniversity of the West of Scotland Paisley, United Kingdom [jose.alcaraz-calero@uws.ac.uk](jose.alcaraz-calero@uws.ac.uk)  \n3rd Qi Wang University of the West of Scotland Paisley, United Kingdom [qi.wang@uws.ac.uk](qi.wang@uws.ac.uk)  \nAbstract—Enhancing the security of Beyond 5G (B5G) and Pre- 6G networks poses significant challenges, particularly in effectively implementing firewalls. Within a wide range of technologies aimed at implementing mitigation mechanisms, achieving optimal technology selection and rule set configuration within these diverse technologies is immensely complex. In addition, these rules are usually based on pre-configured template and lack of optimisation with information of real-time network status, often resulting in sub-optimal configurations. In this paper, an architecture that enables the optimisation of multi-layer multi-technology firewalls integrated in a B5G network testbed is presented. Our proposed framework supports network control monitoring and automatic deployment of firewall rules in three different virtual function implementations: iptables, Open vSwitch and Linux traffic control. After performing a comparison among four popular machine learning (ML) models for the optimal selection, our results show that Random Forest is the best algorithm for the proposed solution with a F1-score of 0.9083.  \nIndex Terms—Firewall optimisation, 5G and beyond network, multi-layer firewall, multi-technology firewall, ML Classifier  \nI. INTRODUCTION  \nThe adoption of beyond B5G networks is creating a new era of connectivity, advancing the three key features defined by the ITU (International Telecommunication Union) including enhanced mobile broadband (eMBB), ultra-reliable and low-latency communications (URLLC) and massive machine type communications (mMTC) [1] . Enhancing these ambitious features have required improvements in terms of network operational efficiency. To this end, further integrating artificial intelligence (AI) with 5G techniques such as Network Function Virtualisation (NFV) and Software-Defined Networking (SDN) is considered key technology enablers for B5G networks [2] . These techniques have changed network service deployment and operation, favouring the softwarisation of network components [3] .  \nAn important task emerging is automating network security functions, which is leading to an unprecedented number of solutions for anomaly detection, firewalling, cyber-attacks discovery, among others. Such solutions trigger the definition and deployment of virtual distributed firewalls in order to mitigate traffic threats. Specifically, rule-based firewalls are the most widely deployed among traditional ones [4] . Firewall rules define how to react to ingress/egress traffic based on predefined security policies. In many cases, the definition of  \nthese firewall rules are human-defined or specified by an intentbased approach. While it is true that, With the automation of B5G services, both location and deployment of the firewall occur automatically. For this reason, virtual firewalls deployed in B5G networks are a type of Virtual Network Function (VNF) as it is a software-based instantiation of a network function that traditionally would have been implemented using dedicated hardware [5] .  \nOptimising rule-based firewalls is critic","cbCaimzt1ApzV8ki","https://ap.wps.com/l/cbCaimzt1ApzV8ki","pdf",333693,1,7,"English","en",105,"# Introduction\n## Beyond 5G features and AI-enabled networking\n## Need for automated firewall optimisation\n## Rule ordering, scale, and human-defined constraints\n# Proposed framework\n## Supervised ML for selecting datapath technology\n## Automatic deployment across iptables, Open vSwitch, and Linux TC\n# Machine learning model comparison\n## Optimal algorithm selection and performance results","[{\"question\":\"Why are template-based firewall rules problematic in Beyond 5G networks?\",\"answer\":\"They are configured without real-time network status information, which can produce sub-optimal rule sets and reduce overall efficiency under changing conditions.\"},{\"question\":\"What does the proposed architecture optimize?\",\"answer\":\"It optimizes multi-layer, multi-technology firewall rule deployment in a Beyond 5G testbed, automatically applying rules through different virtual function implementations.\"},{\"question\":\"Which machine learning model performed best for optimal technology selection?\",\"answer\":\"Random Forest delivered the best results among the compared models, with an F1-score of 0.9083.\"}]","Multi-Layer Multi-Technology Firewall Optimisation in Beyond 5G Networks Using Machine Learning Classifiers | 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are template-based firewall rules problematic in Beyond 5G networks?","Question",{"text":75,"@type":76},"They are configured without real-time network status information, which can produce sub-optimal rule sets and reduce overall efficiency under changing conditions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does the proposed architecture optimize?",{"text":80,"@type":76},"It optimizes multi-layer, multi-technology firewall rule deployment in a Beyond 5G testbed, automatically applying rules through different virtual function implementations.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model performed best for optimal technology selection?",{"text":84,"@type":76},"Random Forest delivered the best results among the compared models, with an F1-score of 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