[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118012-en":3,"doc-seo-118012-105":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},118012,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",6,"Technology","Investigating Security Issues (Multilayer Attacks) on IoT Devices Using Machine Learning","This research investigates security issues, specifically multilayer attacks, on IoT devices through the application of machine learning techniques. The study aims to identify and characterize diverse multilayer security attacks and their behavioral patterns, with a focus on enhancing IoT security. It explores various machine learning algorithms and relevant datasets to improve detection efficiency. Key objectives include investigating ML and datasets that bolster IoT security against these complex attacks, exploring a range of feature selection algorithms, and applying feature weighting to optimize the use of significant features. Furthermore, the research intends to fine-tune hyperparameters for machine learning classification models. The presented methodology involves common feature selection techniques, iterating over attack types, listing features for selected attacks, counting feature occurrences, and identifying common features across different attack vectors. The dataset analysis section provides a comparative overview of several datasets, including KDDCUP 99, NSL-KDD, UNSWNB15, CICIDS2017, BoT-IoT, N-BaIoT, ToN-IoT, and Edge-IIoTset, detailing their year of origin, IoT specificity, total features, total attacks, and types of multilayer attacks they encompass, such as DoS, XSS, SQL Injection, and MITM.","Investigating Security Issues (Multilayer Attacks) on IoT Devices Using Machine Learning  \nPresented by: Badeea AlSukhni  \nSupervisors: SoumyaManna andLeishi Zhang  \n1  \nBackground  \nIDC: International Data Corporation  \nIoT Security Impacts:  \nBackground  \n•Significant financial losses  \n•Reputational damage  \n•Personal information theft  \nBackground   \nStealing  \nMITM Attack sensitive data  \nSmart Healthcare System  \nMITM: Man-in-the-middle attack  \n4  \nThe IoT Security Attacks   \nAl Sukhni, B., Dave, J.M., Manna, S.K. and Zhang, L., 2022, December. Investigating the security issues of multi-layer IoT attacks using machine learning techniques. In 2022 Human-Centered Cognitive Systems (HCCS) (pp. 1-9) . IEEE.  \nAims and Objectives   \nIdentify MultiLayer security attacks and their behavioral patterns.  \nInvestigate ML and datasets that enhance IoT security against multilayer attacks.  \nExplore a variety of feature selection algorithms..  \nApply feature weighting.  \nIncrease detection efficiency by utilizing significant features.  \nFine-tune hyperparameters for ML classification models.  \nObjectives  \n1  \n3  \n4  \n5  \n6  \n2  \nDatasets Analysis   \n\n| Dataset | Year | IoT Specific | Total Features | Total\u003Cbr>Attacks | Multilayer Attacks |\n| --- | --- | --- | --- | --- | --- |\n| KDDCUP 99 | 1999 | No | 41 | 4 | DoS |\n| NSL-KDD | 2009 | No | 43 | 4 | DoS |\n| UNSWNB15 | 2015 | No | 49 | 9 | DoS |\n| CICIDS2017 | 2017 | No | 80 | 14 | DoS, XSS, SQL Injection |\n| BoT-IoT | 2018 | Yes | 45 | 10 | DoS/DDoS |\n| N-BaIoT | 2018 | Yes | 115 | 2 | Botnet attacks (Mirai and Gafgyt) |\n| ToN-IoT | 2020 | Yes | 44 | 9 | DoS/DDoS, SQL Injection, XSS, MITM |\n| Edge-IIoTset | 2022 | Yes | 62 | 14 | DoS/DDoS, SQL Injection, XSS, MITM |\n\nMethodology  \nCommon Feature Selection   \nIterate over attack_type feature  \nFeature listing for selected attack  \nCount feature occurrences  \nIdentify common features","cbCaiiBvvOajumzR","https://ap.wps.com/l/cbCaiiBvvOajumzR","pdf",1968328,1,23,"English","en",105,"# Introduction\n## Background\n## The IoT Security Attacks\n## Aims and Objectives\n## Datasets Analysis\n## Methodology\n### Common Feature Selection","[{\"question\":\"What are the primary objectives of this research?\",\"answer\":\"The primary objectives are to identify multilayer security attacks and their patterns, investigate machine learning and datasets for enhancing IoT security, explore feature selection algorithms, apply feature weighting, and fine-tune ML classification models.\"},{\"question\":\"Which datasets were analyzed in this research?\",\"answer\":\"The research analyzed datasets including KDDCUP 99, NSL-KDD, UNSWNB15, CICIDS2017, BoT-IoT, N-BaIoT, ToN-IoT, and Edge-IIoTset.\"},{\"question\":\"What types of multilayer attacks are discussed in the study?\",\"answer\":\"The study discusses various multilayer attacks such as DoS/DDoS, XSS, SQL Injection, and Man-in-the-Middle (MITM) attacks, as well as botnet attacks like Mirai and Gafgyt.\"}]","Investigating Security Issues (Multilayer Attacks) on IoT Devices Using Machine Learning | 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are the primary objectives of this research?","Question",{"text":76,"@type":77},"The primary objectives are to identify multilayer security attacks and their patterns, investigate machine learning and datasets for enhancing IoT security, explore feature selection algorithms, apply feature weighting, and fine-tune ML classification models.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which datasets were analyzed in this research?",{"text":81,"@type":77},"The research analyzed datasets including KDDCUP 99, NSL-KDD, UNSWNB15, CICIDS2017, BoT-IoT, N-BaIoT, ToN-IoT, and Edge-IIoTset.",{"name":83,"@type":74,"acceptedAnswer":84},"What types of multilayer attacks are discussed in the study?",{"text":85,"@type":77},"The study discusses various multilayer attacks such as DoS/DDoS, XSS, SQL Injection, and Man-in-the-Middle (MITM) attacks, as well as botnet attacks like Mirai and 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