[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128118-en":3,"doc-seo-128118-105":31,"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":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},128118,3985741905716,"Rowan","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Evaluation and Classification Intrusion Detection System for IoT Networks by Using Different Machine Learning Algorithm - Master’s Thesis","Intrusion detection in Internet of Things (IoT) networks is analyzed as a key capability for strengthening cybersecurity. The thesis evaluates multiple machine learning algorithms for assessing and classifying intrusion detection systems using a curated dataset derived from a simulated military network environment containing diverse cyber threats. Decision Tree, Random Forest, XGBoost, and others are compared through classification error metrics and confusion matrices, with Decision Tree reaching 98.58% accuracy. The study emphasizes data collection and existing security measures, and highlights limitations from using a single dataset and omitting required measures, recommending dataset diversification and further training for improved generality and performance.","REPUBLIC OF TÜRKİYE ALTINBAŞ UNIVERSITY  \nInstitute of Graduate Studies Information Technology  \nEVALUATION AND CLASSIFICATION INTRUSION DETECTION SYSTEM FOR IOT NETWORKS BY USING DIFFERENT MACHINE LEARNING ALGORITHM  \nQayssar Dheyaa Mohsin MOHSIN  \nMaster’s Thesis  \nSupervisor Asst. Prof. Dr. Oğuz KARAN  \nİstanbul, 2024  \nEVALUATION AND CLASSIFICATION INTRUSION DETECTION SYSTEM FOR IOT NETWORKS BY USING DIFFERENT MACHINE LEARNING ALGORITHM  \nQayssar Dheyaa Mohsin MOHSIN  \nInformation Technologies  \nMaster’s Thesis  \nALTINBAŞ UNIVERSITY  \nThe thesis titled an EVALUATION AND CLASSIFICATION INTRUSION DETECTION SYSTEM FOR IOT NETWORKS BY USING DIFFERENT MACHINE LEARNING ALGORITHM prepared by QAYSSAR DHEYAA MOHSIN and submitted on 28/06/2024 has been accepted unanimously for the degree Master of Science in Information Technology.  \nAsst. Prof. Dr. Oğuz KARAN Supervisor  \nThesis Defense Committee Members:  \nAsst. Prof. Dr. Oğuz KARAN Faculty Of Engineering and  \nArchitecture,  \nAltinbas University    \nAssoc. Prof. Dr. Sefer KURNAZ Faculty Of Engineering and  \nArchitecture,  \nAltinbas University    \nAsst. Prof. Dr. Abdullahi ABDU IBRAHIM  \nAsst. Prof. Dr. Zeynep ALTAN  \nAsst. Prof. Dr. Serdar KARGIN  \nFaculty Of Engineering and Architecture,  \nAltinbas University Software Engineering, Beykent University  \nFaculty of Biomedical Engineering,  \nIstanbul Arel University  \n__________________  \n__________________  \n__________________  \nI hereby declare that this thesis meets all format and submission requirements of a Master`s Thesis.  \nI hereby declare that all information in this document has been obtained and presented in accordance with academic rules and ethical conduct. I also declare that, as required by these rules and conduct, I have fully cited and referenced all material and results that are not original to this work.  \nQayssar Dheyaa MOHSIN  \nSignature  \nDEDICATION  \nI devote and pledge this research work to my supervisor who is salient for guiding me through whole research work as well as my family for always assisting me in my hard  \ntime.  \nPREFACE  \nFirst and foremost, I would like to thank my supervisor Asst. Prof. Dr. Oğuz KARAN for guiding and helping me along the way in writing this dissertation. Discussing my progress, problems, and ideas with my supervisor Asst. Prof. Dr. Oğuz KARAN a couple of times every week helped me tremendously in understanding the logic behind the research. It  \nmade me better realize the technical need for this research work.  \nABSTRACT  \nEVALUATION AND CLASSIFICATION INTRUSION DETECTION SYSTEM FOR IOT NETWORKS BY USING DIFFERENT MACHINE LEARNING ALGORITHM  \nMOHSIN, Qayssar Dheyaa Mohsin  \nM.Sc., Information Technology, Altınbaş University,  \nSupervisor: Asst. Prof. Dr. Oğuz KARAN  \nDate: June / 2024  \nIntrusion detection in Internet of Things (IoT) networks is a vital part of assuring cybersecurity. This research analyzes the usefulness of several machine learning algorithms in assessing and categorizing intrusion detection systems for IoT networks. The study leverages a rigorously curated dataset, predates a simulated military network environment, containing multiple cyber threats. Random Forest, Voting Regressor, XGBoost, Decision Tree, K-Nearest Neighbor, Linear Regression, and MLP Regressor are the main techniques used in the machine learning field. Significant of Decision Tree algorithm is that it figures highly, displaying 98.58% accuracy. The assessment has an abundant number of results as classification error and confusion matrix. The Decision Tree model is foreseen to be capable of eliminating defects and managing the IoT system in the ideal manner. The method consists of data processing, algorithm mounting, & configuration. After that, classification is tested to evaluate performance. The research takes a notable stand on the collection of data, and the security measures which are in place. Limiting factors in the case study include the use of a single dataset which happens to be t","cbCaipx2bOqiopWX","https://ap.wps.com/l/cbCaipx2bOqiopWX","pdf",3293631,2,1,59,"English","en",105,"# Abstract\n## Methods and Algorithms\n## Dataset and Evaluation Metrics\n## Findings and Limitations\n## Future Work","[{\"question\":\"Which machine learning algorithms are evaluated in the intrusion detection system for IoT networks?\",\"answer\":\"The thesis evaluates Random Forest, Voting Regressor, XGBoost, Decision Tree, K-Nearest Neighbor, Linear Regression, and MLP Regressor.\"},{\"question\":\"How is performance measured for the classification models?\",\"answer\":\"Performance is assessed using classification error results and confusion matrices after data processing and algorithm configuration.\"},{\"question\":\"What result is reported as the strongest outcome?\",\"answer\":\"The Decision Tree algorithm is reported to achieve 98.58% accuracy and is expected to help eliminate defects and manage the IoT system effectively.\"}]","Evaluation and Classification Intrusion Detection System for IoT Networks by Using Different Machine Learning Algorithm - Master’s Thesis | PDF",1785944927,149,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"evaluation-and-classification-intrusion-detection-system-for-iot-networks-by-using-different-machine-learning-algorithm-masters-thesis","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/evaluation-and-classification-intrusion-detection-system-for-iot-networks-by-using-different-machine-learning-algorithm-masters-thesis/128118/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-28","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Which machine learning algorithms are evaluated in the intrusion detection system for IoT networks?","Question",{"text":76,"@type":77},"The thesis evaluates Random Forest, Voting Regressor, XGBoost, Decision Tree, K-Nearest Neighbor, Linear Regression, and MLP Regressor.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is performance measured for the classification models?",{"text":81,"@type":77},"Performance is assessed using classification error results and confusion matrices after data processing and algorithm configuration.",{"name":83,"@type":74,"acceptedAnswer":84},"What result is reported as the strongest outcome?",{"text":85,"@type":77},"The Decision Tree algorithm is reported to achieve 98.58% accuracy and is expected to help eliminate defects and manage the IoT system effectively.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]