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This project addresses unauthorized behavior by focusing on inappropriate access points that attackers can configure, enabling victims to unknowingly connect and exposing valuable information to theft or leakage. A detection system is proposed using machine learning classifiers trained on the Aegean WiFi Intrusion Dataset (AWID), with preprocessing and ANOVA-based feature selection. Evaluations compare classifier accuracy and precision and use the best-performing model for a desktop application that identifies suspicious networks.",{"@graph":69,"@context":127},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/identifying-inappropriate-access-points-using-machine-learning-algorithms-randomforest-and-knn-msc-research-project/290848/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/identifying-inappropriate-access-points-using-machine-learning-algorithms-randomforest-and-knn-msc-research-project/290848.png","ImageObject",300,407,{"name":92,"@type":93},"Skyler","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119,123],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does the project target in wireless networks?","Question",{"text":113,"@type":114},"The project targets the identification of inappropriate or unauthorized access points in WiFi networks, which can be used by attackers to steal or leak sensitive information.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which dataset and feature selection method are used for training?",{"text":118,"@type":114},"Training is based on the Aegean WiFi Intrusion Dataset (AWID), and important features are selected using the analysis of variance (ANOVA) technique.",{"name":120,"@type":111,"acceptedAnswer":121},"Which machine learning classifiers are evaluated?",{"text":122,"@type":114},"The system evaluates K-nearest neighbor (KNN) and Random Forest, along with other mentioned classifiers such as SVM and Genetic Algorithm, then selects the best performer based on accuracy and precision.",{"name":124,"@type":111,"acceptedAnswer":125},"How is the best-performing result intended to be used?",{"text":126,"@type":114},"The best-performing classifier is used to create a desktop application that identifies networks with inappropriate access points from provided network features.","https://schema.org",{"og:url":83,"og:type":129,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":131,"canonical":83},"index,follow",{"doc_id":133,"site_id":62},290848,1789642788,{"code":4,"msg":5,"data":136},{"doc_id":133,"user_id":137,"nickname":92,"user_avatar":138,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":139,"file_id":140,"file_url":141,"file_type":142,"file_size":143,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":144,"language":145,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":146,"faqs":147,"seo_title":148,"seo_description":67,"update_tm":134,"read_time":149,"preview_page_count":4},2336464648746,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Identifying inappropriate access points using machine learning algorithms RandomForest and KNN  \nMSc Research Project Cyber Security  \nTushar Sanjaykumar Vaidya  \nStudent ID: X20254083  \nSchool of Computing  \nNational College of Ireland  \nSupervisor: Prof. Imran Khan  \nNational College of Ireland MSc Project Submission Sheet School of Computing  \nStudent Name:  \nStudent ID:  \nProgramme:  \nModule:  \nSupervisor:  \nSubmission Due Date:  \nProject Title:  \nWord Count:  \nTushar Sanjaykumar Vaidya  \nX20254083  \nMSc Cyber Security 2022-2023  \n……………………………………………………………… Year: …………………………..  \nMSc Research Project  \n\n| Imran Khan |\n| --- |\n| 01-02-23 |\n| Identifying inappropriate access points using machine learning algorithms RandomForest and KNN |\n\n6699  \n……………………………………… Page Count: ……………22…………………….……. .  \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.  \nTushar Sanjaykumar Vaidya  \nSignature:    \n01/02/23  \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 is not 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\nIdentifying inappropriate access points using machine learning algorithms RandomForest and KNN  \nTushar Sanjaykumar Vaidya  \nX20254083  \nMSc in Cybersecurity  \nNational College of Ireland  \nAbstract  \nA wireless network is being used by a wide range of users in today’s world from large organizations to schools to government offices adding to this WiFi hotspots are available at many public places which are used by thousands of people. The wireless network of an organization may facilitate the transfer of highly important and sensitive information and even normal person using a wireless network may transfer their sensitive information through the network. The transfer of large amounts of data has attracted the attention of people who might be looking to steal or leak the sensitive information that is being transferred through a wireless network and these people are able do this by configuring access points that are inappropriate. People may unknowingly connect to these inappropriate access points and may result in the attacker stealing valuable information which may lead to huge financial and personal losses. So, it is important to identify the presence of unauthorized access points in a wireless network and a system is proposed here to do so. The system proposed here will use machine learning classifiers for identifying the presence of unauthorized access points in a wireless network. The system will be developed based on the Aegean WiFi Intrusion Dataset (AWID) intrusion detection dataset. The data will be pre-processed and the important features from the data will be selected using the Analysis of variance (ANOVA) technique. These features will be used for training the machine learning classifiers K-nearest neighbor (KNN), Random Forest, Support Vector Machine (SVM), Genetic Algorithm (GA) . The accuracy and precision ","cbCairYPcaoHbUGf","https://ap.wps.com/l/cbCairYPcaoHbUGf","pdf",665722,22,"English","# Abstract\n# Keywords\n# 1 Introduction","[{\"question\":\"What problem does the project target in wireless networks?\",\"answer\":\"The project targets the identification of inappropriate or unauthorized access points in WiFi networks, which can be used by attackers to steal or leak sensitive information.\"},{\"question\":\"Which dataset and feature selection method are used for training?\",\"answer\":\"Training is based on the Aegean WiFi Intrusion Dataset (AWID), and important features are selected using the analysis of variance (ANOVA) technique.\"},{\"question\":\"Which machine learning classifiers are evaluated?\",\"answer\":\"The system evaluates K-nearest neighbor (KNN) and Random Forest, along with other mentioned classifiers such as SVM and Genetic Algorithm, then selects the best performer based on accuracy and precision.\"},{\"question\":\"How is the best-performing result intended to be used?\",\"answer\":\"The best-performing classifier is used to create a desktop application that identifies networks with inappropriate access points from provided network features.\"}]","Identifying inappropriate access points using machine learning algorithms RandomForest and KNN - MSc Research Project | PDF",55]