[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-290864-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-290864-en":131},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","improving-network-and-iot-intrusion-detection-through-machine-learning-algorithms-msc-practicum-part-2","Improving Network and IoT Intrusion Detection Through Machine Learning Algorithms - MSc Practicum part 2","","Widespread cyberattacks targeting the Internet of Things (IoT) create major operational and security challenges for individuals and organizations. The growing need for these services also introduces risks that anomalies in IoT network data will go undetected, making intrusion detection a central problem. The study evaluates machine learning approaches for detecting traditional network intrusions and IoT log-based threats using two datasets, applying feature selection and validation to address class imbalance and scalability. Random Forest, correlation-based feature selection, and cross-validation/regularization achieve up to 99% performance, while Decision Tree and KNN supervised models reach high accuracy near 99%.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/improving-network-and-iot-intrusion-detection-through-machine-learning-algorithms-msc-practicum-part-2/290864/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/improving-network-and-iot-intrusion-detection-through-machine-learning-algorithms-msc-practicum-part-2/290864.png","ImageObject",300,407,{"name":42,"@type":43},"Skyler","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-29","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",14,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What problem does the document address?","Question",{"text":63,"@type":64},"The document addresses increasing cyberattacks against IoT and the challenge of detecting anomalies and intrusion attempts in IoT and network data in real time.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"Which machine learning methods and datasets are used?",{"text":68,"@type":64},"It evaluates machine learning algorithms using two datasets: an IoT network log dataset and a Network Intrusion log dataset. It discusses feature selection techniques such as Random Forest, correlation coefficient, cross-validation, and regularization, and supervised models including Decision Tree and KNN.",{"name":70,"@type":61,"acceptedAnswer":71},"How does the study handle data imbalance and scalability?",{"text":72,"@type":64},"The document states that feature selection and validation methods (Random Forest, correlation coefficient, cross-validation, and regularization) are used to address class imbalance and scalability concerns in the network and IoT datasets.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},290864,1789642840,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,115,120,123,127],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":112,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":113,"slug":114},8,30,"research-report",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":25,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":25,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":25,"category_name":129,"show_sort_weight":97,"slug":130},19,"General","general",{"code":4,"msg":82,"data":132},{"doc_id":79,"user_id":133,"nickname":42,"user_avatar":134,"doc_module":4,"category_id":112,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":140,"language":141,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":12,"update_tm":80,"read_time":145,"preview_page_count":4},2336464648746,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Improving Network and IoT Intrusion Detection Through Machine Learning  \nAlgorithms  \nMSc Practicum 2 Master of Science in Cybersecurity  \nPrasanth Sriramulu Deenadayala Babu  \nStudent ID: 23173459  \nSchool of Computing  \nNational College of Ireland  \nSupervisor: Vikas Sahni  \nNational College of Ireland  \nMSc Project Submission Sheet  \nSchool of Computing  \nPrasanth Sriramulu Deenadayala Babu  \nStudent    \nName:  \n23173459  \nStudent ID: ……………………………………………………………………………………………………………..……  \nMSC Cyber Security Jan 2024  \nProgramme: ……………………………………………………………… Year: …………………………..  \nModule:  \nSupervisor:  \nSubmission Due Date:  \nProject Title:  \nMSc Practicum part 2  \n\n| Vikas Sahni |\n| --- |\n| 12-12-2024 |\n\nImproving Network and IoT Intrusion Detection Through Machine Learning Algorithms  \n6559 21  \nWord Count: ……………………………………… Page Count…………………………………………….……. .  \nI hereby certify that the information contained in this (my submission) is information pertaining to research I conducted for this project. All information other than my own contribution will be fully referenced and listed in the relevant bibliography section at the rear of the project.  \nALL internet material must be referenced in the bibliography section. Students are required to use the Referencing Standard specified in the report template. To use other author's written or electronic work is illegal (plagiarism) and may result in disciplinary action.  \nPrasanth Sriramulu Deenadayala Babu  \nSignature:    \n12-12-2024  \nDate:    \nPLEASE READ THE FOLLOWING INSTRUCTIONS AND CHECKLIST  \n\n| Attach a completed copy of this sheet to each project (including multiple copies) | □ |\n| --- | --- |\n| Attach a Moodle submission receipt of the online project submission, to each project (including multiple copies) . | □ |\n| You must ensure that you retain a HARD COPY of the project, both for your own reference and in case a project is lost or mislaid. It isnot sufficient to keep a copy on computer. | □ |\n\nAssignments that are submitted to the Programme Coordinator Office must be placed into the assignment box located outside the office.  \n\n| Office Use Only |  |\n| --- | --- |\n| Signature: |  |\n| Date: |  |\n| Penalty Applied (if applicable): |  |\n\nImproving Network and IoT Intrusion Detection Through Machine Learning Algorithms  \nPrasanth Sriramulu Deenadayala Babu  \nX23173459  \nAbstract  \nWidespread and increased cyberattack against Internet of Things (IoT) are causing enormous range of problem for individual and organizations. The growing need for these services has a possibility to contain anomalies in the IoT data network has emerged as a key challenge. This research evaluates machine learning algorithms for detecting both traditional network intrusion and IoT network logs. Two datasets have been used IoT network log and Network Intrusion log dataset to classify various models on the performance metrics. To deal the issue of class imbalance and scalability nature in network and IoT dataset, Feature selection like Random Forest (RF), Correlation Coefficient and Cross-Validation & Regularization has achieved 99%, by improving this real-time processing combined with effective anomaly detection ensures threats are identified and mitigated quickly. Supervised learning models Decision Tree and KNN model has shown high accuracy of 99%. The findings show the capacity for modifying the machine learning technique to achieve high accuracy to identify labeled malicious in network traffic and IoT logs.  \n1 Introduction  \nThe Internet of Things (IoT) is making a paradigm shift in various sectors including healthcare, smart homes, transport, or industrial automation meaning that devices can connect and interact seamlessly over networks across multiple domains. The State ofIoT Summer 2024 brought out by IoT Analytics that included a total of 171 pages has highlighted that there are 16.6 billion IoT devices connected at the end of 2023 with 15% of the increase over 2022. IoT Analytics forecasts this to move o","cbCaihqBYMzqOD7s","https://ap.wps.com/l/cbCaihqBYMzqOD7s","pdf",873540,21,"English","# Abstract\n# 1 Introduction\n## 1.1 Motivation","[{\"question\":\"What problem does the document address?\",\"answer\":\"The document addresses increasing cyberattacks against IoT and the challenge of detecting anomalies and intrusion attempts in IoT and network data in real time.\"},{\"question\":\"Which machine learning methods and datasets are used?\",\"answer\":\"It evaluates machine learning algorithms using two datasets: an IoT network log dataset and a Network Intrusion log dataset. It discusses feature selection techniques such as Random Forest, correlation coefficient, cross-validation, and regularization, and supervised models including Decision Tree and KNN.\"},{\"question\":\"How does the study handle data imbalance and scalability?\",\"answer\":\"The document states that feature selection and validation methods (Random Forest, correlation coefficient, cross-validation, and regularization) are used to address class imbalance and scalability concerns in the network and IoT datasets.\"}]","Improving Network and IoT Intrusion Detection Through Machine Learning Algorithms - MSc Practicum part 2 | PDF",53]