[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126097-en":3,"doc-seo-126097-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126097,5909887256941,"Levi","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Real-time Network Flow Control using Machine Learning and OVS - Bachelor’s thesis","Real-time Network Flow Control using Machine Learning and OVS defines a pipeline for detecting real-time communication by analyzing network traffic using deep packet inspection and (extended) IP flows. The work designs and evaluates a machine-learning-based detector, develops a software prototype, and transfers the model into a lower-level implementation. Detected traffic is then used for traffic prioritisation in Open vSwitch, with performance assessed through accuracy metrics and functional validation of the prototype in an OVS-based proof of concept.","Title:  \nAssignment of bachelor’s thesis  \nReal-time Network Flow Control using Machine Learning and OVS  \nStudent: Štěpán Šimek  \nSupervisor: Ing. Karel Hynek  \nStudy program: Informatics  \nBranch / specialization: Computer Security and Information technology  \nDepartment: Department of Computer Systems  \nValidity: until the end of summer semester 2022/2023  \nInstructions  \nGet acquainted with network monitoring methods based on deep packet inspection and (extended) IP flows.  \nAnalyse the area of Real-Time communication technology and protocols, focus on their detection possibilities based on traffic characteristics.  \nDesign an algorithm for automatic detection of real-time communication based on Machine Learning. Develop a software prototype capable of its detection. Use the information from the detector for traffic prioritisation in Open vSwitch (OVS). Evaluate the accuracy of the machine learning-based algorithm and the proper functionality of the prototype.  \nElectronically approved by prof. Ing. Pavel Tvrdík, CSc. on 30 January 2022 in Prague.  \nBachelor’s thesis  \nREAL-TIME NETWORK FLOW CONTROL USING MACHINE LEARNING AND OVS  \ntˇep´an imek  \nFaculty of Information Technology Department of Computer Systems Supervisor: Ing. Karel Hynek May 11, 2023  \nCzech Technical University in Prague Faculty of Information Technology  \n© 2023 ˇStˇep´an ˇSimek. All rights reserved.  \nThis thesis is school work as defined by Copyright Act of the Czech Republic. It has been submitted at Czech Technical University in Prague, Faculty of Information Technology. The thesis is protected by the  \nCopyright Act and its usage without author’s permission is prohibited (with exceptions defined by the Copyright Act) .  \nCitation of this thesis: ˇSimek ˇStˇep´an. Real-time Network Flow Control using Machine Learning and O VS. Bachelor’s thesis. Czech Technical University in Prague, Faculty of Information Technology, 2023.  \nContents  \nAcknowledgments viii  \nDeclaration ix  \nAbstract x  \nList of abbreviations xi  \nIntroduction 1  \n1 Background 3  \n1.1 Network traffic monitoring ............................... 3  \n1.1.1 Packet based-Deep Packet Inspection . . . . . . . . . . . . . . . . . . . . 3  \n1.1.2 IP Flow based . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4  \n1.2 Network monitoring architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 4  \n1.2. 1 IPFIX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5  \n1.3 VoIP real-time traffic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6  \n1.3. 1 WebRTC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6  \n1.3.2 RTP protocol and its derivatives . . . . . . . . . . . . . . . . . . . . . . . 7  \n1.3.3 SRTP and MS Teams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10  \n1.4 Machine learning methods for traffic recognition . . . . . . . . . . . . . . . . . . 11  \n1.4. 1 Decision Trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12  \n1.4.2 Trees ensembles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13  \n1.5 Open vSwitch   14  \n1.5. 1 OpenFlow protocol   14  \n1.5.2 Open vSwitch and flows   14  \n2 Dataset creation 17  \n2.1 Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17  \n2.1.1 Modules design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17  \n2.2 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19  \n2.2. 1 DPI classifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19  \n2.2.2 DPI exporter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20  \n2.3 Data collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21  \n2.3. 1 Local traffic capture   21  \n2.3.2 Core-network traffic capture   22  \n2.4 Resulting dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2","cbCaidCShUWEx8l6","https://ap.wps.com/l/cbCaidCShUWEx8l6","pdf",1995533,10,1,69,"English","en",105,"# Introduction\n## Background\n## Dataset creation\n## Machine learning design\n# Implementation of traffic prioritization\n# Evaluation","[{\"question\":\"What detection inputs and monitoring approaches does the thesis use?\",\"answer\":\"It uses network monitoring methods based on deep packet inspection and (extended) IP flows, including IPFIX concepts, to derive traffic features for recognition of real-time communication.\"},{\"question\":\"How does the machine learning detector support traffic prioritisation in OVS?\",\"answer\":\"The detector classifies real-time communication from traffic characteristics; 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