[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123026-en":3,"doc-seo-123026-105":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},123026,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","A Systematic Investigation on Botnet Intrusion Detection Using Various Machine Learning Techniques","The Internet of Things (IoT) is expanding rapidly, but its heterogeneous devices face growing cybersecurity risks from widely prevalent botnet-based attacks. Limited memory and computational capacity make IoT environments difficult to defend, while discovering unknown intrusion patterns in IoT-generated network data remains a core research challenge. The study classifies incoming IoT data as malicious or benign using machine learning, and proposes an ML-based botnet intrusion detection framework targeting nine commercial IoT devices. Evaluation uses the N-BaIoT dataset and demonstrates efficient detection with broader applicability.","JOE International Journal of  \nOnline and Biomedical Engineering  \n[Onli](Online-Journals.org)[ne-Jo](Online-Journals.org)[urnals](Online-Journals.org)[.org](Online-Journals.org)  \niJOE | eISSN: 2626-8493 | Vol. 20 No. 10 (2024) |   \n[https://doi.org/10.3991/ijoe.v20i10.49509](https://doi.org/10.3991/ijoe.v20i10.49509)  \nPAPER  \nA Systematic Investigation on Botnet Intrusion  \nDetection Using Various Machine Learning Techniques  \nArchana Kalidindi(􀀍), Mahesh Babu Arrama  \nDepartment of CSE, Koneru Lakshmaiah Education Foundation, Hyderabad, Telangana, India  \narchana.buddaraju@ [klh.edu.in](klh.edu.in)  \nABSTRACT  \nThe Internet of Things (IoT) is growing rapidly in an exponential manner due to its versatility in technology. This has led to many challenges in securing the IoT environment. Devices in IoT environments are vulnerable to various cyberattacks. Botnet-based attacks are predominant and widespread in nature. Due to insufficient memory and computational power, the IoT environment cannot handle the botnet attack that affects security. Identifying intrusions in IoT environments is another challenge for researchers. Finding unknown patterns in the data generated through IoT networks helps improve security in the IoT environment. Machine learning (ML) is a platform that helps identify patterns in the provided data. In this study, we present our research on classifying incoming data from the IoT as malicious or benign using machine learning techniques. We propose an ML-based botnet attack detection framework for nine commercial IoT devices that primarily target BASHLITE and Mirai botnet attacks. Rigorous pragmatic research was conducted on the N-BaIoT dataset, which was extracted from realtime IoT devices connected to a network. Using this framework, the results have been depicted, which can efficiently detect botnet attacks and can also be applied to any other types of attacks.  \nKEYWORDS  \nInternet of Things (IoT), botnet detection, machine learning (ML), N-BaIoT  \n1 INTRODUCTION  \nThe basis for the evolution of Industry 4.0 was the remarkable achievements of Industry 3.0. Industry 4.0 mechanisms and methods have revolutionized the processes used in all product-based companies. This trend has driven industries to integrate various new technologies, such as the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), cloud-based computing, and more, into their operational processes. IoT is the prominent key to enabling the technology revolution [1] . The IoTis a vast universal information system comprising numerous diverse and distributed devices interconnected through the Internet. These devices can be identified, and the data from them can be sensed, processed, and shared with each other through a smart  \nKalidindi, A., Arrama, M. B. (2024) . A Systematic Investigation on Botnet Intrusion Detection Using Various Machine Learning Techniques. International Journal of Online and Biomedical Engineering (iJOE), 20(10), pp. 18–32. [https://doi.org/10.3991/ijoe.v20i10.49509](https://doi.org/10.3991/ijoe.v20i10.49509)[ ](https://doi.org/10.3991/ijoe.v20i10.49509)[Article submitted 2024-04-04. Revision uploaded 2024-05-18. Final acceptance 2024-05-20.](Article submitted 2024-04-04. Revision uploaded 2024-05-18. Final acceptance 2024-05-20.)  \n© 2024 by the authors of this article. Published under CC-BY.  \n18 International Journal of Online and Biomedical Engineering (iJOE) iJOE | Vol. 20 No. 10 (2024)  \nA Systematic Investigation on Botnet Intrusion Detection Using Various Machine Learning Techniques  \nenvironment [2] . IoT is widely used in sectors such as healthcare, logistics, smart homes, smart cities, and supply chain management. According to an article in IoT Business News, the number of smart devices used in the IoT environment is projected to increase by 20 times in the near future, reaching an estimated 24.1 billion by 2030.  \nThe coexistence and usage of these smart devices inthe IoT environment hav","cbCaig8rsc76Ihh8","https://ap.wps.com/l/cbCaig8rsc76Ihh8","pdf",1779672,1,15,"English","en",105,"# Introduction\n## IoT growth and security challenges\n## Botnet attacks and representative malware\n# Related Work (inferred)\n## Malware taxonomy and intrusion concepts\n# Methodology (inferred)\n## ML-based detection framework\n## Dataset: N-BaIoT\n# Results (inferred)\n## Detection performance and discussion\n# Conclusion (inferred)\n## Findings and applicability to other attacks","[{\"question\":\"What problem does the study address in IoT security?\",\"answer\":\"It addresses the difficulty of detecting botnet-driven intrusions in IoT environments where devices have limited computational resources and where identifying unknown attack patterns is challenging.\"},{\"question\":\"How does the proposed system detect botnet attacks?\",\"answer\":\"It classifies incoming IoT data as malicious or benign using machine learning techniques within an ML-based detection framework.\"},{\"question\":\"Which attacks and dataset are used for evaluation?\",\"answer\":\"The framework is evaluated on nine commercial IoT devices with focus on BASHLITE and Mirai botnet attacks, using the N-BaIoT dataset extracted from real-time IoT devices connected to a network.\"}]","A Systematic Investigation on Botnet Intrusion Detection Using Various Machine Learning Techniques | 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problem does the study address in IoT security?","Question",{"text":75,"@type":76},"It addresses the difficulty of detecting botnet-driven intrusions in IoT environments where devices have limited computational resources and where identifying unknown attack patterns is challenging.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed system detect botnet attacks?",{"text":80,"@type":76},"It classifies incoming IoT data as malicious or benign using machine learning techniques within an ML-based detection framework.",{"name":82,"@type":73,"acceptedAnswer":83},"Which attacks and dataset are used for evaluation?",{"text":84,"@type":76},"The framework is evaluated on nine commercial IoT devices with focus on BASHLITE and Mirai botnet attacks, using the N-BaIoT dataset extracted from real-time IoT devices connected to a 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