[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120999-en":3,"doc-seo-120999-105":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":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},120999,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Evaluation of Machine Learning and Deep Learning Methods for Early Detection of Internet of Things Botnets","The internet of things (IoT) links large numbers of smart devices autonomously, expanding both capabilities and security exposure. This work addresses IoT botnet threats by comparing machine learning and deep learning approaches for early detection and by mapping botnet-related risks to an attack life-cycle taxonomy. It consolidates recent advancements, reviews current mitigation strategies, highlights influential datasets, and identifies future research directions and open challenges for strengthening intelligence-driven IoT security frameworks.","International Journal of Electrical and Computer Engineering (IJECE)  \nVol. 14, No. 4, August 2024, pp. 4732∼4744  \nISSN: 2088-8708, DOI: 10.11591/ijece.v14i4.pp4732-4744 ❒ 4732  \n\n| Evaluation of machine learning and deep learning methods for early detection of internet of things botnets\u003Cbr>Ashraf Suleiman Mashaleh1,2 , Noor Farizah Ibrahim1 , Mohammad Alauthman3 , Jamal Al-Karaki4,5 , Ammar Almomani2,6 , Shadi Atalla7 , Amjad Gawanmeh7\u003Cbr>1 School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang, Malaysia\u003Cbr>2Computer Center, Al-Balqa’ Applied University, As-Salt, Jordan\u003Cbr>3Department of Information Security, Faculty of information technology, University of Petra, Amman, Jordan\u003Cbr>4College of Interdisciplinary Studies, Zayed University, Abu Dhabi, United Arab Emirates\u003Cbr>5Department of Computer Engineering, College of Engineering, Hashemite University, Zarqa, Jordan\u003Cbr>6Research and Innovation Department, Skyline University College, Sharjah, United Arab Emirates\u003Cbr>7College of Engineering and IT, University of Dubai, Dubai, United Arab Emirates |  |  |\n| --- | --- | --- |\n| Article Info\u003Cbr>Article history:\u003Cbr>Received Jan 18, 2023 Revised Mar 19, 2024 Accepted Apr 2, 2024\u003Cbr>Keywords:\u003Cbr>Big data\u003Cbr>Big data analytics Healthcare\u003Cbr>Internet of things Personalised healthcare Point-of-care devices |  | ABSTRACT\u003Cbr>The internet of things (IoT) represents a rapidly expanding sector within computing, facilitating the interconnection of myriad smart devices autonomously. However, the complex interplay ofIoT systems and their interdisciplinary nature has presented novel security concerns (e.g. privacy risks, device vulnerabilities, Botnets) . In response, there has been a growing reliance on machine learning and deep learning methodologies to transition from conventional connectivitycentric IoT security paradigms to intelligence-driven security frameworks. This paper undertakes a comprehensive comparative analysis of recent advancementsin the creation of IoT botnets. It introduces a novel taxonomy of attacks structured around the attack life-cycle, aiming to enhance the understanding and mitigation of IoT botnet threats. Furthermore, the paper surveys contemporary techniques employed for early-stage detection of IoT botnets, with a primary emphasis on machine learning and deep learning approaches. This elucidates the current landscape of the issue, existing mitigation strategies, and potential avenues for future research.\u003Cbr>This is an open access article under the CC BY-SA license. |\n| Corresponding Author: |  |  |\n| Amjad Gawanmeh\u003Cbr>Department of Electrical Engineering, College of Engineering and IT, University of Dubai Dubai, United Arab Emirates\u003Cbr>Email: [amjad.gawanmeh@ieee.org](amjad.gawanmeh@ieee.org) |  |  |\n\n1. INTRODUCTION  \nThe internet of things (IoT) has reformed environmental sensing. It can increase life quality by collecting, quantifying, and understanding the environment. The term internet of things, abbreviated as IoT, is becoming a highly appealing buzzword among all individuals associated with practitioners and users of technology, including businesses and their clients. Technology plays a significant role in changing people’s lives and significantly influences the workplace, where sensitive information is shared over the Internet. As a result, the attacker’s curiosity can be turned into monetary gain. To achieve their objectives, attackers utilize numerous types of malware. Botnets are among the most dangerous forms of malware for unethical Internet activity. IoT device security measures like firewalls and access control mechanisms focus on data confidentiality and authenticity, network access control, security, and privacy policy innovation to build trust [1] .  \nBotnet is robot and network. The Botnet began on internet relay chat (IRC), a text-based chat system that separates conversations into channels, where bots did not always mean evil things [2] . Malware-infested computers, known as bots","cbCaiki1AFnt7DWx","https://ap.wps.com/l/cbCaiki1AFnt7DWx","pdf",1405436,1,13,"English","en",105,"# Introduction\n## IoT security challenges and botnet overview\n## Detection methods by botnet life-cycle stage\n## Paper contributions and scope\n# Early-stage detection approaches\n## Machine learning and deep learning review\n## Datasets and evaluation focus","[{\"question\":\"What problem does the paper focus on?\",\"answer\":\"The paper focuses on early detection of IoT botnets and how machine learning and deep learning can be used to move from conventional connectivity-centric IoT security to intelligence-driven frameworks.\"},{\"question\":\"How does the paper organize IoT botnet attacks?\",\"answer\":\"It introduces a taxonomy of attacks structured around the attack life-cycle, helping explain evolution across botnet stages and informing mitigation.\"},{\"question\":\"What time range of research and what datasets are discussed?\",\"answer\":\"The review covers recent IoT botnet detection and mitigation work from 2018–2022 and investigates datasets used in IoT security, including both normal and unusual activity.\"}]","Evaluation of Machine Learning and Deep Learning Methods for Early Detection of Internet of Things Botnets | 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problem does the paper focus on?","Question",{"text":75,"@type":76},"The paper focuses on early detection of IoT botnets and how machine learning and deep learning can be used to move from conventional connectivity-centric IoT security to intelligence-driven frameworks.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper organize IoT botnet attacks?",{"text":80,"@type":76},"It introduces a taxonomy of attacks structured around the attack life-cycle, helping explain evolution across botnet stages and informing mitigation.",{"name":82,"@type":73,"acceptedAnswer":83},"What time range of research and what datasets are discussed?",{"text":84,"@type":76},"The review covers recent IoT botnet detection and mitigation work from 2018–2022 and investigates datasets used in IoT security, including both normal and unusual 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