[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124080-en":3,"doc-seo-124080-105":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},124080,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",6,"Technology","IoT Security - Botnet Detection Using Self-Organizing Feature Map and Machine Learning","The rapid advancement of Internet of Things (IoT) technology enables progress across daily life, while the growing number of connected devices increases exposure to cyberattacks, especially IoT botnets. Limitations in device capacity, power, and memory require an efficient detection approach. This study builds a resource-efficient botnet detection system by combining SOFM dimensionality reduction with machine learning classification, then evaluates accuracy, sensitivity, specificity, FPR, and FNR. The Decision Tree achieves 97.24% accuracy, while SOFM reduces memory usage from 3.08 GB to 923 MB.","Accredited SINTA 2 Ranking  \nDecree of the Director General of Higher Education, Research, and Technology, No. 158/E/KPT/2021 Validity period from Volume 5 Number 2 of 2021 to Volume 10 Number 1 of 2026  \nPublished online at: [http://jurnal.iaii.or.id](http://jurnal.iaii.or.id)  \n\n|  | JURNAL RESTI\u003Cbr>(Rekayasa Sistem dan Teknologi Informasi)\u003Cbr>Vol. 8 No. 6 (2024) 788-798 e-ISSN: 2580-0760 |\n| --- | --- |\n\nIoT Security: Botnet Detection Using Self-Organizing Feature Map and  \nMachine Learning  \nSusanto 1*, Deris Stiawan2, Budi Santoso3, Alex Onesimus Sidabutar4, M. Agus Syamsul A5, Mohd. Yazid Idris6,  \nRahmat Budiarto7,  \n1,3,4Department of Informatica, Faculty of Engineering Science, Universitas Bina Insan, Lubuklinggau, Indonesia  \n2Department of Computer Engineering, Faculty of Computer Science, Universitas Sriwijaya, Palembang, Indoensia  \n5Department of Computer Engineering, Faculty of Engineering Science, Universitas Bina Insan, Lubuklinggau, Indonesia  \n6School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia  \n7College of Computer Science and IT, Al-Baha University, Alaqiq, Saudi Arabia  \n[1](1 susanto@univbinainsan.ac.id)[ susanto@univbinainsan.ac.id](1 susanto@univbinainsan.ac.id), [2](2 deris@unsri.ac.id)[ deris@unsri.ac.id](2 deris@unsri.ac.id), [3](3budisantoso@univbinainsan.ac.id)[budisantoso@univbinainsan.ac.id](3budisantoso@univbinainsan.ac.id), [4](4 alextkj12@gmail.com)[ alextkj12@gmail.com](4 alextkj12@gmail.com),  \n[5](5 mas.arifin@univbinainsan.ac.id)[ mas.arifin@univbinainsan.ac.id](5 mas.arifin@univbinainsan.ac.id), [6](6 yazid@utm.my)[ yazid@utm.my](6 yazid@utm.my), [7](7 rahmat@bu.edu.sa)[ rahmat@bu.edu.sa](7 rahmat@bu.edu.sa)  \nAbstract  \nThe rapid advancement of Internet of Things (IoT) technology has created potential for progress in various aspects of life. However, the increasing number of IoT devices also raises the risk of cyberattacks, particularly IoT botnets often exploited by attackers. This is largely due to the limitations ofIoT devices, such as constraints in capacity, power, and memory, necessitating an efficient detection system. This study aims to develop a resource-efficient botnet detection system by using the SelfOrganizing Feature Map (SOFM) dimensionality reduction method in combination with machine learning algorithms. The proposed method includes a feature engineering process using SOFM to address high-dimensional data, followed by classification with various machine learning algorithms. The experiments evaluate performance based on accuracy, sensitivity, specificity, False Positive Rate (FPR), and False Negative Rate (FNR). Results show that the Decision Tree algorithm achieved the highest accuracy rate of 97.24%, with a sensitivity of 0.9523, specificity of 0.9932, and a fast execution time of 100.66 seconds. The use of SOFM successfully reduced memory consumption from 3.08 GB to 923MB. Experimental results indicate that this approach is effective for enhancing IoT security in resource-constrained devices.  \nKeywords: Botnet; IoT; Feature Engineering; SOFM; Machine Learning  \nHow to Cite: Susanto,“IoT Security: Botnet Detection Using Self-Organizing Feature Map and Machine Learning”, J. RESTI (Rekayasa Sist. Teknol. Inf.) , vol. 8, no. 6, pp. 788-798, Dec. 2024.  \nDOI: [https://doi.org/10.29207/resti.v8i6.5871](https://doi.org/10.29207/resti.v8i6.5871)  \n1. Introduction  \nThe swift progress and adoption of intelligent and Internet of Things (IoT)-based technologies have facilitated numerous potential advancements across various facets of life [1] . This has become a new paradigm that transforms traditional lifestyles into hightech living. The IoT has brought about transformationsin the form of smart transportation, smart cities pollution control, energy savings, smart homes, and smart industries [2] . On the other hand, the rapid expansion of IoT devices has resulted in a rise in cyberattacks aimed at these devices [3] . Attacks util","cbCaiqBRAvg7Qcwq","https://ap.wps.com/l/cbCaiqBRAvg7Qcwq","pdf",469680,1,11,"English","en",105,"# Introduction\n## IoT adoption and security challenges\n## IDS role and high-dimensional data problem\n## Feature engineering via dimensionality reduction (SOFM)\n## Contribution and evaluation metrics","[{\"question\":\"Why is botnet detection important in IoT networks?\",\"answer\":\"IoT devices are increasingly targeted by cyberattacks, and attackers commonly exploit IoT botnets. Resource constraints and limited compatible security solutions make detection systems especially necessary.\"},{\"question\":\"How does the proposed method use SOFM in botnet detection?\",\"answer\":\"SOFM performs feature engineering by reducing high-dimensional data into a more manageable representation. This reduced representation is then used for classification by multiple machine learning algorithms.\"},{\"question\":\"Which algorithm performed best and what were the key results?\",\"answer\":\"The Decision Tree achieved the highest accuracy of 97.24%, with sensitivity 0.9523, specificity 0.9932, and a fast execution time of 100.66 seconds. SOFM also reduced memory consumption from 3.08 GB to 923 MB.\"}]","IoT Security - Botnet Detection Using Self-Organizing Feature Map and Machine Learning | PDF",1785820224,28,{"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},"iot-security-botnet-detection-using-self-organizing-feature-map-and-machine-learning","",{"@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/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/iot-security-botnet-detection-using-self-organizing-feature-map-and-machine-learning/124080/",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},"Why is botnet detection important in IoT networks?","Question",{"text":75,"@type":76},"IoT devices are increasingly targeted by cyberattacks, and attackers commonly exploit IoT botnets. Resource constraints and limited compatible security solutions make detection systems especially necessary.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method use SOFM in botnet detection?",{"text":80,"@type":76},"SOFM performs feature engineering by reducing high-dimensional data into a more manageable representation. This reduced representation is then used for classification by multiple machine learning algorithms.",{"name":82,"@type":73,"acceptedAnswer":83},"Which algorithm performed best and what were the key results?",{"text":84,"@type":76},"The Decision Tree achieved the highest accuracy of 97.24%, with sensitivity 0.9523, specificity 0.9932, and a fast execution time of 100.66 seconds. SOFM also reduced memory consumption from 3.08 GB to 923 MB.","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},[93,97,101,105,110,113,118,123,128,131,135],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",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"]