[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120116-en":3,"doc-seo-120116-105":30,"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":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},120116,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","IoT Attacks Detection Using Supervised Machine Learning Techniques - Flood and Brute Force","IoT devices increasingly support daily life, but they face cybersecurity threats similar to those on traditional networks because they rely on connectivity and synchronization. This study detects Flood and Brute Force cyberattacks using machine learning and deep learning models with an emphasis on identifying traffic features that distinguish these attack types. Eight models are evaluated via accuracy, precision, recall, and F1-score. Two experiment sets are run using six features and, after feature selection, three reduced features, with Gradient Boosting achieving the highest accuracy (95.94% and 95.28%).","IoT Attacks Detection Using Supervised Machine Learning Techniques  \nMalak Aljabri 1, Afrah Shaahid 2*, Fatima Alnasser 2, Asalah Saleh 2 , Dorieh Alomari 2, Menna Aboulnour 2 , Walla Al-Eidarous 1 , Areej Althubaity 3  \n1 Department of Computer and Network Engineering, College of Computing, UmmAl-Qura University, Makkah 21955, Saudi Arabia.  \n2 College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia.  \n3 Depatment of Cybersecurity, College of Computing, UmmAl-Qura University, Makkah 21955, Saudi Arabia.  \nReceived 29 March 2024; Revised 29 July 2024; Accepted 08 August 2024; Published 01 September 2024  \nAbstract  \nIn recent times, the growing significance of Internet of Things (IoT) devices in people's lives is undeniable, driven by their myriad benefits. However, these devices confront cybersecurity threats akin to traditional network devices, as they depend on networks for connectivity and synchronization. Artificial Intelligence (AI) techniques, specifically Machine Learning (ML) and Deep Learning (DL), have demonstrated notable reliability in the field of cyberattack detection. This study focuses on detecting Flood and Brute Force cyberattacks using Machine Learning (ML) and Deep Learning (DL) models. The primary emphasis lies in identifying traffic features that significantly detect these types of attacks. The experimental study incorporates eight models: Decision Tree (DT), K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machines (SVM), Logistic Regression (LR), Gradient Boosting (GB), Naïve Bayes (NB), and Artificial Neural Network (ANN) . Two sets of experiments were conducted, with the first set involving six features and the subsequent set, after feature selection, focusing on a reduced set of three features. The evaluation of the proposed model's efficiency and performance relied on metrics such as Accuracy, Precision, Recall, and F1-score. Remarkably, all proposed models exhibited high performance in both sets of experiments. However, the Gradient Boosting (GB) classifier suppressed others,  \nattaining an impressive accuracy level of 95.94% and 95.28% in the sets with six features and three features, respectively. Keywords: Supervised; IoT Security; Cyberattacks; IoT Attacks.  \n1. Introduction  \nIn the contemporary era, the internet has become a fundamental aspect of our daily existence. Attempts to breach computer systems and networks have escalated due to the surge in online applications that evolved with the advent of transformative technologies like the Internet of Things (IoT). IoT, seamlessly integrating intelligent objects and devices, has experienced exponential growth, projecting a global connection of 15.1 billion devices in 2023 [1] . The range ofIoT applications extends from wearables for health monitoring and smart fridges in home appliances to intelligent boards for education [2]. Nonetheless, IoT confronts a range of cyber threats in the internet's hostile environment, emphasizing the ongoing need for efforts to support network security. Machine Learning (ML) emerges as a highly successful computational model for embedding Artificial Intelligence (AI) in the IoT domain. ML methods in cybersecurity have been instrumental in various network security advancements [3], including network traffic analysis [4-6], intrusion detection [7], and botnet identification [8-10] .  \n* Corresponding author: [2190009057@iau.edu.sa](2190009057@iau.edu.sa)  \n [http://dx.doi.org/10.28991/HIJ-2024-05-03-01](http://dx.doi.org/10.28991/HIJ-2024-05-03-01)  \nØ This is an open access article under the CC-BY license ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \n© Authors retain all copyrights.  \nML plays a crucial role in IoT solutions with its unique ability to automate or adapt knowledge-based behaviors. It can unearth valuable insights from data generated by humans or machines. ","cbCaid7IJAYOEzFS","https://ap.wps.com/l/cbCaid7IJAYOEzFS","pdf",1325523,1,17,"English","en",105,"# Abstract\n# Introduction\n## IoT security threats and the need for detection\n## Machine learning for attack detection\n## Two attack types: Flood DoS and Brute Force","[{\"question\":\"Which cyberattacks does the study focus on detecting?\",\"answer\":\"The study targets Flood denial of service (DoS) attacks and RTSP brute force attacks.\"},{\"question\":\"How many models and which algorithms are evaluated?\",\"answer\":\"Eight models are evaluated: Decision Tree, K-Nearest Neighbor, Random Forest, Support Vector Machines, Logistic Regression, Gradient Boosting, Naïve Bayes, and Artificial Neural Network.\"},{\"question\":\"What experimental feature settings are compared?\",\"answer\":\"Two experiment sets are used: one with six traffic features and a second with three features after feature selection.\"}]","IoT Attacks Detection Using Supervised Machine Learning Techniques - Flood and Brute Force | PDF",1785728289,43,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"iot-attacks-detection-using-supervised-machine-learning-techniques-flood-and-brute-force","",{"@graph":36,"@context":86},[37,54,69],{"@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/iot-attacks-detection-using-supervised-machine-learning-techniques-flood-and-brute-force/120116/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",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 cyberattacks does the study focus on detecting?","Question",{"text":76,"@type":77},"The study targets Flood denial of service (DoS) attacks and RTSP brute force attacks.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How many models and which algorithms are evaluated?",{"text":81,"@type":77},"Eight models are evaluated: Decision Tree, K-Nearest Neighbor, Random Forest, Support Vector Machines, Logistic Regression, Gradient Boosting, Naïve Bayes, and Artificial Neural Network.",{"name":83,"@type":74,"acceptedAnswer":84},"What experimental feature settings are compared?",{"text":85,"@type":77},"Two experiment sets are used: one with six traffic features and a second with three features after feature selection.","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":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]