[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127205-en":3,"doc-seo-127205-105":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},127205,549768702563,"Sage","https://ap-avatar.wpscdn.com/avatar/8000c4aa63b76e948b?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786536092046926083",8,"Research & Report","MACHINE LEARNING-BASED ANOMALY DETECTION IN NETWORKS - AN ENSEMBLE LEARNING PERSPECTIVE","This thesis investigates how combining machine learning with Software Defined Networks (SDN) strengthens Network Intrusion Detection Systems (NIDS) against continuously evolving security threats. It examines the role of Big Data Analytics (BDA) and highlights applications spanning disaster response and healthcare contexts. The work emphasizes machine learning methods for extracting insights from social media analysis and supports the need for innovative NIDS designs that protect modern data-driven networks.","REPUBLIC OF TÜRKİYE ALTINBAŞ UNIVERSITY  \nInstitute of Graduate Studies  \nInformation Technologies  \nMACHINE LEARNING-BASED ANOMALY  \nDETECTION IN NETWORKS: AN ENSEMBLE  \nLEARNING PERSPECTIVE  \nWafa Ashour Ali SALIM  \nMaster's Thesis  \nSupervisor  \nAssoc. Prof. Dr. Sefer KURNAZ  \nİstanbul, 2024  \nMACHINE LEARNİNG-BASED ANOMALY DETECTİON IN NETWORKS: AN ENSEMBLE LEARNING PERSPECTIVE  \nWafa Ashour Ali SALIM  \nInformation Technologies  \nMaster's Thesis  \nALTINBAŞ UNIVERSITY  \nThe thesis titled MACHINE LEARNING-BASED ANOMALY DETECTION IN NETWORKS: AN ENSEMBLE LEARNING PERSPECTIVE Prepared by WAFA ASHOUR ALI SALIM and submitted on 11/07/2024 has been accepted unanimously for the degree of Master of Science in Information Technology.  \nAssoc. Prof. Dr. Sefer KURNAZ  \nSupervisor  \nThesis Defense Committee Members:  \nAssoc. Prof. Dr. Sefer KURNAZ  \nAsst. Prof. Dr. Abdullahi Abdu  \nIBRAHIM  \nDepartment of Computer  \nEngineering,  \nAltınbaş University  \nDepartment of Computer  \nEngineering,  \nAltınbaş University  \n__________________  \n__________________  \nAsst. Prof. Dr. Serdar KARGIN Department of  \nBiomedical Engineering,  \nIstanbul Arel University    \nI hereby declare that this thesis meets all format and submission requirements ofa master's Thesis.  \nI hereby declare that all information/data presented in this graduation project has been obtained in full accordance with academic rules and ethical conduct. I also declare all unoriginal materials and conclusions have been cited in the text and all references mentioned in the Reference List have been cited in the text, and vice versa as required by the abovementioned rules and conduct.  \nWafa Ashour Ali SALIM  \nSignature  \nDEDICATION  \nI dedicate this thesis for my advisor Assoc. Prof. Dr. Sefer KURNAZ. He always support me during my study until to graduated and get my my degree. Also, I would like to dedicate my thesis to all my family, friends, colleagues and for every one stand beside me and encourage me.  \nABSTRACT  \nMACHINE LEARNING-BASED ANOMALY DETECTION IN NETWORKS: AN ENSEMBLE LEARNING PERSPECTIVE  \nSALIM, Wafa Ashour Ali  \nM.Sc., Information Technologies, Altınbaş University  \nSupervisor: Assoc. Prof. Dr. Sefer KURNAZ  \nDate: 07/2024  \nPages: 59  \nThis thesis investigates the fusion of Machine Learning and Software Defined Networks  \n(SDN) in Network Intrusion Detection Systems (NIDS) to tackle evolving security  \ncomplexities. It explores the power of Big Data Analytics (BDA) in disaster response and  \nhealthcare, showcasing machine learning's role in social media analysis. The thesis  \nunderlines the imperative for innovative NIDS solutions in safeguarding modern data-driven  \nnetworks.  \nKeywords: Software Defined Networks, Innovation, NIDS, Anomaly Detection, Deep Learning.  \nÖZET  \nAĞLARDA MAKİNE ÖĞRENME TABANLI ANORMALLİKTESPİTİ: BİR TOPLULUK ÖĞRENME PERSPEKTİFİ  \nSALIM, Wafa Ashour Ali  \nYüksek Lisans, Bilişim Teknolojileri, Altınbaş Üniversitesi  \nDanışman: Doç . Dr. Sefer KURNAZ  \nTarih: 07/2024  \nSayfa: 59  \nBu tez, gelişen güvenlik karmaşıklıklarının üstesinden gelmek için Ağ Saldırı Tespit Sistemlerinde (NIDS) Makine Öğrenimi ve Yazılım Tanımlı Ağların (SDN) birleşimini  \naraştırmaktadır. Afet müdahalesi ve sağlık hizmetlerinde Büyük Veri Analitiğinin (BDA)  \ngücünü araştırıyor ve makine öğreniminin sosyal medya analizindeki rolünü ortaya koyuyor.  \nTez, modern veri odaklı ağların korunmasında yenilikçi NIDS çözümlerinin zorunluluğunun  \naltını çiziyor.  \nAnahtar Kelimeler: Yazılım Tanımlı Ağlar, İnovasyon, NIDS, Anomali Tespiti, Derin Öğrenme.  \nTABLE OF CONTENTS  \nPages  \n[ABSTRACT ........................................................................................................................ vi](ABSTRACT ........................................................................................................................ vi)  \nÖZET .........................................................................................................................","cbCaijHiRiFbm4W6","https://ap.wps.com/l/cbCaijHiRiFbm4W6","pdf",1256017,1,64,"English","en",105,"# Introduction\n## Background and Motivation\n## Research Motivation\n## Problem Statement\n## Research Objectives\n## Report Structure\n# Literature Review\n## Anomaly Detection in Network Systems\n## Anomaly Classification\n## Machine Learning\n## Deep Learning\n## Random Forest\n## Support Vector Machine\n## Naive Bayes","[{\"question\":\"What is the main focus of the thesis?\",\"answer\":\"The thesis focuses on improving network intrusion detection by fusing machine learning with Software Defined Networks (SDN).\"},{\"question\":\"How does the thesis position anomaly detection for NIDS?\",\"answer\":\"It studies how machine learning enables anomaly detection in network systems to respond to evolving security complexities.\"},{\"question\":\"Which application areas does the thesis discuss for analytics and learning?\",\"answer\":\"It highlights disaster response and healthcare, and it also discusses the use of machine learning for social media analysis.\"}]","MACHINE LEARNING-BASED ANOMALY DETECTION IN NETWORKS - 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