[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119368-en":3,"doc-seo-119368-105":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},119368,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Machine Learning Algorithms to Defend Against Routing Attacks on the Internet of Things - A Systematic Literature Review","The Internet of Things (IoT) expands connectivity across domains such as smart cities, healthcare, manufacturing, and agriculture, relying on low-power constrained devices in Low Power and Lossy Networks (LLNs). The IETF’s RPL routing protocol, while enabling routing of observed data, is vulnerable to multiple routing attacks that degrade network performance. A systematic literature review synthesizes 17 publications to compare traditional and advanced machine learning approaches for detecting RPL-based attacks. Results show high detection accuracy with low false positives, with Random Forest achieving over 99% accuracy, precision, and recall.","Machine Learning Algorithms to Defend Against Routing Attacks on the Internet of Things: A Systematic Literature  \nReview  \nLanka Chris Sejaphala1, Vusimuzi Malele2, Francis Lugayizi3  \n1,2Department of Computer Science and Information Systems, North West University, Vanderbijlpark, South Africa  \n3 Department of Computer Science and Information Systems, North West University, Mmabatho,  \nSouth Africa  \n[Email:](Email:1 chris.sejaphala@nwu.ac.za)[1](Email:1 chris.sejaphala@nwu.ac.za)[ chris.sejaphala@nwu.ac.za](Email:1 chris.sejaphala@nwu.ac.za), [2](2vusi.malele@nwu.ac.za)[vusi.malele@nwu.ac.za](2vusi.malele@nwu.ac.za), [3](3 francis.lugayizi@nwu.ac.za)[ francis.lugayizi@nwu.ac.za](3 francis.lugayizi@nwu.ac.za)  \nAbstract  \nThe Internet of Things (IoT) has become increasingly popular, opening vast application possibilities in different fields including smart cities, healthcare, manufacturing, agriculture, etc. IoT comprises resource-constrained devices deployed in Low Power and Lossy Networks (LLNs) . To satisfy the routing requirements of these networks, the Internet Engineering Task Force (IETF) created a standardised Routing Protocol for low-power and Lossy Networks (RPL) . However, this routing protocol is vulnerable to routing attacks, prompting researchers to propose several techniques to defend the network against such attacks. Machine learning approaches demonstrate effective ways to detect such attacks in large quantities. Therefore, this paper systematically synthesised 17 publications to compare the performance of traditional and advanced machine learning algorithms to identify the best algorithm for detecting RPL-based IoT routing attacks. The findings of this paper show that machine learning algorithms are capable of effective detection of many routing attacks with high accuracy and a low False Positive Rate. Furthermore, the results demonstrate that on average, advanced machine learning algorithms can achieve an accuracy of 96.03% compared to traditional machine learning algorithms which achieved 91.67%. Traditional machine learning algorithms demonstrated the best performance on average False Positive Rate by achieving 2.75% compared to their counterparts which gained 4.79%. However, Random Forest showed the best performance and outperformed all the algorithms in the selected publications by achieving over 99% accuracy, precision and recall.  \nKeywords: RPL, IoT, LNNs, Machine learning, routing attacks  \n1. INTRODUCTION  \nThe IoT is a paradigm of interconnected devices which collect and exchange data with each other from an environment of deployment and share the data over the  \ninternet to achieve a particular goal [1] . This paradigm is used in a wide range of applications including home security management, industrial automation, smart energy monitoring and management, surveillance and military, smart cities, and farming, etc.  \nDue to its characteristics and nature, IoT has limitations regarding energy, memory, and computational capabilities, which traditional routing protocols cannot satisfy[2] . The Internet Engineering Task Force (IETF) working group designed and standardised Routing protocol for low-power and Lossy networks (RPL) to satisfy the routing needs of Low Power and Lossy Networks (LLNS) and to enable the resource-constrained devices to communicate their routing information among themselves and route their observed data to the root node[3, 4] . However, RPL as the DE facto routing protocol in IoT is susceptible to different routing attacks (i.e. flooding, sinkhole, worst parent attacks, etc) [5] . Routing attacks pose a great threat to the RPL-based IoT and can affect its performance and functionalities [4] .  \nDifferent defence techniques against routing attacks in RPL-based IoT have been studied in the recent past, including the secure protocol, IDS and machine learning-based [6-8] . Machine learning techniques are currently new and more effective techniques used to deal with routing attacks ","cbCaij0Ktzqc0qhL","https://ap.wps.com/l/cbCaij0Ktzqc0qhL","pdf",763907,1,16,"English","en",105,"# Introduction\n## IoT, LLNs, and the role of RPL\n## Routing attacks and defense approaches\n## Study objective and contribution\n# Literature Review\n# Systematic Literature Review Methodology\n# Findings and Analysis\n# Conclusion","[{\"question\":\"Why is RPL vulnerable in IoT, and what types of routing attacks matter?\",\"answer\":\"RPL is susceptible to routing attacks such as flooding, sinkhole, and worst parent attacks. These attacks threaten RPL-based IoT performance and functionality.\"},{\"question\":\"What is the main objective of the systematic literature review in this paper?\",\"answer\":\"The paper uses a systematic literature review method to identify the best-performing machine learning algorithm for detecting routing attacks in RPL-based IoT.\"},{\"question\":\"Which machine learning approach performed best according to the included studies?\",\"answer\":\"Random Forest showed the best overall performance, outperforming other algorithms in the selected publications with over 99% accuracy, precision, and recall.\"}]","Machine Learning Algorithms to Defend Against Routing Attacks on the Internet of Things - A Systematic Literature Review | PDF",1785723950,40,{"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},"machine-learning-algorithms-to-defend-against-routing-attacks-on-the-internet-of-things-a-systematic-literature-review","",{"@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/machine-learning-algorithms-to-defend-against-routing-attacks-on-the-internet-of-things-a-systematic-literature-review/119368/",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","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is RPL vulnerable in IoT, and what types of routing attacks matter?","Question",{"text":75,"@type":76},"RPL is susceptible to routing attacks such as flooding, sinkhole, and worst parent attacks. These attacks threaten RPL-based IoT performance and functionality.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the main objective of the systematic literature review in this paper?",{"text":80,"@type":76},"The paper uses a systematic literature review method to identify the best-performing machine learning algorithm for detecting routing attacks in RPL-based IoT.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning approach performed best according to the included studies?",{"text":84,"@type":76},"Random Forest showed the best overall performance, outperforming other algorithms in the selected publications with over 99% accuracy, precision, and recall.","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},[93,97,101,105,110,115,119,122,127,130,134],{"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":29,"slug":118},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]