[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124160-en":3,"doc-seo-124160-105":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},124160,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Detection and Classification of Anonymous Traffic using Machine Learning","Traffic classification is a core capability for network management, monitoring, and service design, since traffic characteristics enable better performance and quality of service. In anonymized settings, classification becomes more complex due to packet encryption and the growing deployment of anonymity tools. This dissertation investigates how machine learning algorithms can perform multilevel anonymous traffic classification, adapting an intrusion detection system as a proof of concept using mixed benign and anonymized datasets and comparing results with earlier empirical studies.","University of Minho  \nSchool of Engineering  \nMaria Miguel Albuquerque Regueiras  \nDetection and Classification of Anonymous Traffic using Machine Learning  \napril 2024  \nUniversity of Minho  \nSchool of Engineering  \nMaria Miguel Albuquerque Regueiras  \nDetection and Classification of Anonymous Traffic using Machine Learning  \nIntegrated Master’s in Informatics Engineering  \nDissertation supervised by  \nProfessor Paulo Manuel Martins Carvalho  \nProfessor Maria Solange Pires Ferreira Rito Lima  \napril 2024  \nCopyright and Terms of Use for Third Party Work  \nThis dissertation reports on academic work that can be used by third parties as long as the internationally accepted standards and good practices are respected concerning copyright and related rights.  \nThis work can thereafter be used under the terms established in the license below.  \nReaders needing authorization conditions not provided for in the indicated licensing should contact the author through the RepositóriUM of the University of Minho.  \nLicense granted to users of this work:  \nCC BY  \n[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \nAcknowledgements  \nThere are many people who supported me during not only the process of writing this dissertation but also these academic years.  \nFirstly, this dissertation could not have been possible without Professors Paulo Carvalho, Maria Solange Lima and João Marco Silva. It was as long a road for them as it was for me. Thank you for all the motivation, availability and guidance during this time.  \nTo my parents and brother, for always believing in me and providing me everything I needed up until this point. Thank you for having patience when I did not have it myself.  \nTo my boyfriend, Alexandre, for the continuous bright mood when things were tougher. Thank you for always being there and helping me trust in my own abilities.  \nAnd to my great colleagues and friends, Bruno and Luís, for being the ones which I have shared the most moments with during these years. Thank you for all the laughs (and car rides) .  \nThis dissertation marks the end of a chapter and, with it, many memories are bound to it. However, as all paths, it was not always a smooth one. It certainly had its challenges. Thus, I would like to also leave a special reminder for myself, for what lies ahead: courage is found in unlikely places. This is proof it is always possible.  \nI’m glad you were all here with me, at the end of this journey.  \nThank you all! Maria Miguel Regueiras  \nStatement of Integrity  \nI hereby declare having conducted this academic work with integrity.  \nI confirm that I have not used plagiarism or any form of undue use of information or falsification of results along the process leading to its elaboration.  \nI further declare that I have fully acknowledged the Code of Ethical Conduct of the University of Minho.  \nUniversity of Minho, Braga, april 2024  \nMaria Miguel Albuquerque Regueiras  \nAbstract  \nTraffic classification is a fundamental task for various aspects of network management and monitoring. Knowing traffic characteristics allows a better design of the network and services, contributing for an improved performance and quality of service. Regarding anonymized traffic, this becomes an intricate procedure due to packet encryption.  \nThe presence of anonymous traffic in networks is noticeable, demonstrating user’s concern about privacy and anonymity at different levels. The growing use of anonymity tools creates the need of a secure and efficient experience. Therefore, traffic classification is also relevant to developers of these tools (to improve user service robustness) and Internet Service Providers (to understand what type of traffic is circulating in the network) .  \nThis dissertation’s primary goal is to study the application of machine learning algorithms in multilevel anonymous traffic classification. This work proposes an adaptation of an intrusion detection system as proof of ","cbCaicPQYSVN3ujR","https://ap.wps.com/l/cbCaicPQYSVN3ujR","pdf",1821727,1,89,"English","en",105,"# Introduction\n## Motivation\n## Objectives\n## Research Questions","[{\"question\":\"Why is traffic classification important for network management?\",\"answer\":\"Traffic classification supports better network and service design by revealing traffic characteristics, improving performance and quality of service.\"},{\"question\":\"What makes traffic classification harder for anonymized traffic?\",\"answer\":\"Anonymized traffic introduces packet encryption, which complicates distinguishing and analyzing traffic patterns.\"},{\"question\":\"What is the dissertation’s main goal and approach?\",\"answer\":\"It studies applying machine learning to multilevel anonymous traffic classification, proposing an intrusion detection system adaptation using mixed benign and anonymized datasets and comparing with prior empirical studies.\"}]","Detection and Classification of Anonymous Traffic using Machine Learning | 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