[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120496-en":3,"doc-seo-120496-105":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},120496,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","ANOMALY-BASED INTRUSION DETECTION SYSTEMS USING MACHINE LEARNING ALGORITHM","The increasing number of cyber-attacks increases the need for intrusion detection systems (IDS) capable of accurately and efficiently detecting and classifying attacks. Machine learning methods can learn from network data to discover patterns associated with intrusions or anomalies. This research aims to find a highly accurate, error-tolerant classifier for identifying anomalous traffic in a network intrusion detection dataset. Seven machine learning techniques are evaluated using accuracy, precision, recall, and F1-score, enabling comparison of strengths and weaknesses to support stronger, more dependable defenses for digital networks.","REPUBLIC OF TÜRKİYE ALTINBAŞ UNIVERSITY  \nInstitute of Graduate Studies Electrical and Computer Engineering  \nANOMALY-BASED INTRUSION DETECTION SYSTEMS USING MACHINE LEARNING  \nALGORTIHIM  \nKarrar Ali Awad AL-JUBOORI  \nMaster’s Thesis  \nSupervisor Prof. Dr. Osman Nuri UÇAN  \nIstanbul, 2023  \nANOMALY-BASED INTRUSION DETECTION SYSTEMS USING  \nMACHINE LEARNING ALGORITHM  \nKarrar Ali Awad AL-JUBOORI  \nElectrical and Computer Engineering  \nMaster’s Thesis  \nALTINBAŞ UNIVERSITY  \n2023  \nThis thesis title” ANOMALY-BASED INTRUSION DETECTION SYSTEMS USING MACHINE LEARNING ALGORITHM” prepared by KARRAR ALI AWAD AL-JUBOORI and submitted on 5/4/2023 has been accepted unanimously for the degree of Master of Science in Electrical and Computer Engineering.  \nProf. Dr. Osman Nuri UÇAN Supervisor  \nThesis Defence Committee Members:  \nProf. Dr. Osman Nuri UÇAN  \nAsst. Prof. Dr. Abdullahi Abdu  \nIBRAHIM  \nDepartment of Electrical and Electronic Engineering,  \nAltinbas University  \nDepartment of Computer  \n__________________  \nEngineering,  \nAltinbas University    \nAsst. Prof. Dr. Serdar KARGIN Department of Electronics  \nand  \nCommunication  \nEngineering,  \nIstanbul Arel University  \n__________________  \nI hereby declare that this thesis meets all format and submission requirements ofa Master’s thesis. Submission date of the thesis to institute of Graduate Studies:  / /   \nBy signing this declaration, I vouch for the fact that all information and resources used in this capstone project were acquired with the utmost adherence to moral standards and academic regulations. All references listed in the Reference List and vice versa have been cited in accordance with the aforementioned standards of conduct. I also attest to the accurate citation of all conclusions and borrowed ideas throughout the article.  \nKarrar Ali Awad AL-JUBOORI  \nSignature  \nDEDICATION  \nTo My Country “IRAQ”  \nTo my dear father and my role model in life, who inspired me to continue my scientific path, and to my beloved wife and daughter, who accompanied me at all times and were the blessings of Support and help…My Mother, Brothers and Sisters.  \nABSTRACT  \nANOMALY-BASED INTRUSION DETECTION SYSTEMS USING MACHINE LEARNING ALGORITHM  \nAL-JUBOORI, Karrar Ali Awad  \nM.Sc., Electrical and Computer Engineering, Altinbas University,  \nSupervisor: Prof. Dr. Osman Nuri UÇAN  \nDate: April / 2023  \nPage: 85  \nThe increasing number of cyber-attacks highlights the need for improved intrusion detection  \nsystems (IDS) that can detect and classify attacks accurately and efficiently. Machine  \nlearning (ML) techniques have been shown to be effective in this regard, as they can learn  \nfrom data and identify patterns that may indicate an intrusion or anomaly.  \nIn this research, we want to find a highly accurate and error-tolerant classifier for identifying aberrant traffic in a network intrusion detection dataset. We will evaluate the effectiveness of seven different machine learning techniques on a given dataset by using various evaluation metrics such as accuracy, precision, recall, and F1-score. These measurements allow us to evaluate the effectiveness of various algorithms and offer insightful information about the advantages and disadvantages of each approach.  \nOur ultimate goal is to select the best-performing classifier that can accurately detect anomalous traffic with minimal error. By identifying an effective classifier for NIDS, our goal is to aid in the creation of stronger, more dependable systems that can defend against cyberattacks and guarantee the safety of digital networks.  \nKeywords: Instruction Detection System (IDS) , Network IDS, Machine Learning, Cyber Security.  \nTABLE OF CONTENTS  \nPages  \n[ABSTRACT ........................................................................................................................ vi](ABSTRACT ........................................................................................................................ vi)  \n[LIST OF FIGU","cbCaivmytnEqpTCF","https://ap.wps.com/l/cbCaivmytnEqpTCF","pdf",3485667,1,90,"English","en",105,"# 1. INTRODUCTION\n## 1.1 BACKGROUND\n## 1.2 INTRODUCTION TO INTRUSION\n## 1.2.1 Intrusion Detection\n## 1.2.2 Various Methods of Intrusion\n## 1.3 PROCESS OF USING TOOLS & METHODS\n## 1.3.1 Intrusion Detection System\n## 1.3.2 Classification of Intrusion Detection System\n## 1.4 THESIS OBJECTIVES\n## 1.5 THESIS CONTRIBUTION\n## 1.6 PROPOSED WORK\n## 1.7 EXPECTED RESULTS\n## 1.8 THESIS OUTLINE","[{\"question\":\"What problem does the thesis address in network security?\",\"answer\":\"It addresses the need for improved intrusion detection systems that can detect and classify cyber-attacks accurately and efficiently, especially by identifying anomalous traffic.\"},{\"question\":\"How does the research use machine learning to detect intrusions?\",\"answer\":\"It evaluates seven machine learning techniques that learn from network data and identify patterns indicating intrusions or anomalies.\"},{\"question\":\"What evaluation metrics are used to compare the classifiers?\",\"answer\":\"The study compares approaches using accuracy, precision, recall, and F1-score to assess performance and error tolerance.\"}]","ANOMALY-BASED INTRUSION DETECTION SYSTEMS USING MACHINE LEARNING ALGORITHM | PDF",1785730361,227,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"anomaly-based-intrusion-detection-systems-using-machine-learning-algorithm","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/anomaly-based-intrusion-detection-systems-using-machine-learning-algorithm/120496/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the thesis address in network security?","Question",{"text":75,"@type":76},"It addresses the need for improved intrusion detection systems that can detect and classify cyber-attacks accurately and efficiently, especially by identifying anomalous traffic.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the research use machine learning to detect intrusions?",{"text":80,"@type":76},"It evaluates seven machine learning techniques that learn from network data and identify patterns indicating intrusions or anomalies.",{"name":82,"@type":73,"acceptedAnswer":83},"What evaluation metrics are used to compare the classifiers?",{"text":84,"@type":76},"The study compares approaches using accuracy, precision, recall, and F1-score to assess performance and error tolerance.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,96,100,104,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":21,"slug":95},"Story & Novel","story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":105,"slug":137},19,"General","general"]