[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124168-en":3,"doc-seo-124168-105":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},124168,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Man-in-the-middle and denial of service attacks detection using machine learning algorithms - Detection and evaluation results","Network attacks, including man-in-the-middle (MTM) and denial of service (DoS), enable unauthorized access and data theft from connected devices. The study uses datasets for MTM and DoS attacks obtained from Kaggle, then applies preprocessing such as imputing missing values caused by many null entries. Four machine learning models—random forest, XGBoost, gradient boosting, and decision tree—are evaluated using precision, accuracy, recall, and f1-score. Results show MTM detection above 99% across metrics and DoS detection above 97% across metrics, indicating strong capability for protecting devices.","Man-in-the-middle and denial of service attacks detection using  \nmachine learning algorithms  \nSura Abdulmunem Mohammed Al-Juboori1, Firas Hazzaa1, Zinah Sattar Jabbar2, Sinan Salih2,  \nHassan Muwafaq Gheni3  \n1Ministry of Higher Education and Scientific Research, Baghdad, Iraq  \n2Department of Communication Technology Engineering, College of Information Technology, Imam Ja'afar Al-Sadiq University,  \nBaghdad, Iraq  \n3Department Computer Techniques Engineering, Al-Mustaqbal University College, Hillah, Iraq  \n\n| Article history:\u003Cbr>Received Aug 19, 2022 Revised Oct 1, 2022 Accepted Oct 20, 2022 | Network attacks (i.e., man-in-the-middle (MTM) and denial of service (DoS) attacks) allow several attackers to obtain and steal important data from physical connected devices in any network. This research used several machine learning algorithms to prevent these attacks and protect the devices by obtaining related datasets from the Kaggle website for MTM and DoS attacks. After obtaining the dataset, this research applied preprocessing techniques like fill the missing values, because this dataset contains a lot of null values. Then we used four machine learning algorithms to detect these attacks: random forest (RF), eXtreme gradient boosting (XGBoost), gradient boosting (GB), and decision tree (DT) . To assess the performance of the algorithms, there are many classification metrics are used: precision, accuracy, recall, and f1-score. The research achieved the following results in both datasets: i) all algorithms can detect the MTM attack with the same performance, which is greater than 99% in all metrics; and ii) all algorithms can detect the DoS attack with the same performance, which is greater than 97% in all metrics. Results showed that these algorithms can detect MTM and DoS attacks very well, which is prompting us to use their effectiveness in protecting devices from these attacks.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr> |\n| --- | --- |\n| Keywords:\u003Cbr>Attacks detection Classification metrics Computer networks and communications\u003Cbr>DoS attack\u003Cbr>Machine learning MTM attack |  |\n\nCorresponding Author:  \nSura Abdulmunem Mohammed Al-Juboori Ministry of Higher Education and Scientific Research Baghdad, Iraq  \nEmail: [sura.sultan.ss@gmail.com](sura.sultan.ss@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nThe internet of factors (IoT) is a idea of connecting thousands and thousands of devices over the net to exchange and percentage facts between those devices, like sensors, mobile phones, laptops, or actuators [1], [2] . These gadgets can interact with each other the use of many one-of-a-kindwireless verbal exchange strategies like Bluetooth, c084d04ddacadd4b971ae3d98fecfb2a, and ZigBee [1], [2] . The IoT has evolved because many a couples of technologies are converging, which includes commodity sensors, machine gaining knowledge of, embedded structures, and ubiquitous computing [3], [4] . Its miles stricken by several sorts of attacks to obtain and thieve statistics, like man-in-the-middle (MTM), adware, sq. injection, denial of provider, social engineering, and ransomware [3] . The man-in-the-center MTM is a 9aaf3f374c58e8c9dcdd1ebf10256fa5 assault, and its miles a cyber-attack in which the attacker discreetly transmits and may alternate the communications among two sufferers who expect they're interacting without delay with each different because the attacker has positioned himself among sufferers [5]–[8] . Simplest whilst the attacker mimics every sufferer nicely wi-fi to satisfy their  \nexpectancies can MTM defeat mutual authentication in any community [5]–[8] . So, this assault could be very risky if it attacks the community that has critical information on its linked devices [5]–[8] . Every other 9aaf3f374c58e8c9dcdd1ebf10256fa5 assault that attacks the community known as denial of service (DoS) . DoSis a cyber-assault that the attacker attempts to render a device or community supply inaccessible","cbCaio9eNyzGqkm9","https://ap.wps.com/l/cbCaio9eNyzGqkm9","pdf",403736,1,9,"English","en",105,"# Keywords\n# Article history\n# Introduction\n## IoT and common network attacks\n## MTM attack concept\n## DoS attack concept\n# Proposed contribution and methodology\n## Dataset acquisition and preprocessing\n## Models used for detection\n## Evaluation metrics\n# Related work","[{\"question\":\"What attacks are targeted in this research?\",\"answer\":\"The research targets man-in-the-middle (MTM) and denial of service (DoS) attacks. These attacks enable interception of communications and making services unavailable.\"},{\"question\":\"Which machine learning algorithms are used for detection?\",\"answer\":\"Four algorithms are used: random forest (RF), eXtreme gradient boosting (XGBoost), gradient boosting (GB), and decision tree (DT).\"},{\"question\":\"How is the model performance evaluated?\",\"answer\":\"Performance is assessed using classification metrics including precision, accuracy, recall, and f1-score. The reported results achieve over 99% for MTM and over 97% for DoS across metrics.\"}]","Man-in-the-middle and denial of service attacks detection using machine learning algorithms - Detection and evaluation results | PDF",1785820829,23,{"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},"man-in-the-middle-and-denial-of-service-attacks-detection-using-machine-learning-algorithms-detection-and-evaluation-results","",{"@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/man-in-the-middle-and-denial-of-service-attacks-detection-using-machine-learning-algorithms-detection-and-evaluation-results/124168/",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","2026-08-04",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 attacks are targeted in this research?","Question",{"text":75,"@type":76},"The research targets man-in-the-middle (MTM) and denial of service (DoS) attacks. These attacks enable interception of communications and making services unavailable.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms are used for detection?",{"text":80,"@type":76},"Four algorithms are used: random forest (RF), eXtreme gradient boosting (XGBoost), gradient boosting (GB), and decision tree (DT).",{"name":82,"@type":73,"acceptedAnswer":83},"How is the model performance evaluated?",{"text":84,"@type":76},"Performance is assessed using classification metrics including precision, accuracy, recall, and f1-score. 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