[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123697-en":3,"doc-seo-123697-105":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},123697,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Performance Evaluation of Machine Learning Approaches in Detecting IoT-Botnet Attacks","Botnets present advanced network vulnerability threats by coordinating botmaster-driven Command and Control frameworks that remotely control zombie machines. They contribute to spam, DDoS, and malware propagation, making reliable detection essential for IoT environments. This study evaluates six machine learning methods on the BoT-IoT dataset (REPTree, RandomTree, RandomForest, J48, metaBagging, and Naive Bayes). Performance is assessed using accuracy, TPR, and FPR across three testing scenarios featuring full parameters, IG feature reduction, and attacker-packet extracted features.","JIM International Journal of  \nInteractive Mobile Technologies  \n[Onli](Online-Journals.org)[ne-Jo](Online-Journals.org)[urnals](Online-Journals.org)[.org](Online-Journals.org)  \niJIM | eISSN: 1865-7923 | Vol. 17 No. 19 (2023) |   \n[https://doi.org/10.3991/ijim.v17i19.41379](https://doi.org/10.3991/ijim.v17i19.41379)  \nPAPER  \nPerformance Evaluation of Machine Learning Approaches in Detecting IoT-Botnet Attacks  \nAshraf H. Aljammal1(*), Ahmad Qawasmeh1, Ala Mughaid2, Salah Taamneh1, Fadi I. Wedyan3, Mamoon Obiedat2  \n1Department of Computer Science and Applications, Prince Al Hussein bin Abdullah II Faculty of Information Technology, The Hashemite University, Zarqa, Jordan  \n2Department of Information Technology, Prince Al Hussein bin Abdullah II Faculty of Information Technology, The Hashemite University, Zarqa, Jordan  \n3Department of Engineering, Computing, and Mathematical Sciences, Lewis University, Romeoville, Illinois, USA  \n[ashrafj@hu.edu.jo](ashrafj@hu.edu.jo)  \nABSTRACT  \nBotnets are today recognized as one of the most advanced vulnerability threats. Botnets control a huge percentage of network traffic and PCs. They have the ability to remotely control PCs (zombie machines) by their creator (BotMaster) via Command and Control (C&C) framework. They are the keys to a variety of Internet attacks such as spams, DDOS, and spreading malwares. This study proposes a number of machine learning techniques for detecting botnet assaults via IoT networks to help researchers in choosing the suitable ML algorithm for their applications. Using the BoT-IoT dataset, six different machine learning methods were evaluated: REPTree, RandomTree, RandomForest, J48, metaBagging, and Naive Bayes. Several measures, including accuracy, TPR, FPR, and many more, have been used to evaluate the algorithms’ performance. The six algorithms were evaluated using three different testing situations. Scenario-1 tested the algorithms utilizing all of the parameters presented in the BoT-IoT dataset, scenario-2 used the IG feature reduction approach, and scenario-3 used extracted features from the attacker’s received packets. The results revealed that the assessed algorithms performed well in all three cases with slight differences.  \nKEYWORDS  \nInternet of Things, botnet detection, IoT botnet attack, machine learning, network security, cyber security  \n1 INTRODUCTION  \nThe Internet of Things (IoT) are physical devices connected with each other over a network; these devices are able to collect and share data with other devices [1, 2] . The IoT devices are low power consumption devices, which makes them suitable for many applications in many sectors [3] . In addition, they have the ability to be used for remotely monitoring, controlling, and managing equipment and systems, which results in enhancing efficiency and reduce costs. Smart home systems are an example of IoT home utilization, which allows the home owners to remotely monitor and  \nAljammal, A. H., Qawasmeh, A., Mughaid, A., Taamneh, S., Wedyan, F.I., Obiedat, M. (2023) . Performance Evaluation of Machine Learning Approaches in Detecting IoT-Botnet Attacks. InternationalJournal of Interactive Mobile Technologies (iJIM), 17(19), pp. 136–146.  [https://doi.org/10.3991/ijim.v17i19.41379](https://doi.org/10.3991/ijim.v17i19.41379)[ ](https://doi.org/10.3991/ijim.v17i19.41379)[Article submitted 2023-05-14. Revision uploaded 2023-08-02. Final acceptance 2023-08-08.](Article submitted 2023-05-14. Revision uploaded 2023-08-02. Final acceptance 2023-08-08.)  \n© 2023 by the authors of this article. Published under CC-BY.  \n136 International Journal of Interactive Mobile Technologies (iJIM) iJIM | Vol. 17 No. 19 (2023)  \nPerformance Evaluation of Machine Learning Approaches in Detecting IoT-Botnet Attacks  \noperate their home appliances [4] . Another example is the wearable devices that can collect health and fitness information to be used later in providing users with advice in terms of nutrition and exercise ","cbCailhINOxzDSrM","https://ap.wps.com/l/cbCailhINOxzDSrM","pdf",626170,1,11,"English","en",105,"# Introduction\n# Related Work and Background\n# Methodology and Experimental Setup\n## Dataset: BoT-IoT\n## Machine Learning Models\n## Evaluation Metrics\n## Testing Scenarios\n# Results and Performance Comparison\n# Conclusion","[{\"question\":\"What is the main goal of this study?\",\"answer\":\"The study evaluates machine learning approaches for detecting IoT-botnet attacks so researchers can select suitable algorithms for their applications.\"},{\"question\":\"Which dataset and models are used in the evaluation?\",\"answer\":\"The BoT-IoT dataset is used to evaluate six models: REPTree, RandomTree, RandomForest, J48, metaBagging, and Naive Bayes.\"},{\"question\":\"How are the algorithms tested in different scenarios?\",\"answer\":\"Scenario-1 uses all BoT-IoT parameters, scenario-2 applies IG feature reduction, and scenario-3 relies on extracted features from the attacker’s received packets.\"}]","Performance Evaluation of Machine Learning Approaches in Detecting IoT-Botnet Attacks | PDF",1785818068,28,{"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},"performance-evaluation-of-machine-learning-approaches-in-detecting-iot-botnet-attacks","",{"@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/performance-evaluation-of-machine-learning-approaches-in-detecting-iot-botnet-attacks/123697/",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 is the main goal of this study?","Question",{"text":75,"@type":76},"The study evaluates machine learning approaches for detecting IoT-botnet attacks so researchers can select suitable algorithms for their applications.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which dataset and models are used in the evaluation?",{"text":80,"@type":76},"The BoT-IoT dataset is used to evaluate six models: REPTree, RandomTree, RandomForest, J48, metaBagging, and Naive Bayes.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the algorithms tested in different scenarios?",{"text":84,"@type":76},"Scenario-1 uses all BoT-IoT parameters, scenario-2 applies IG feature reduction, and scenario-3 relies on extracted features from the attacker’s received packets.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]