[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-86616-en":3,"doc-seo-86616-105":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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},86616,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Optimized Intrusion Detection for IoT Networks Using Machine Learning and Feature Selection with RTGBO-ELM Integration","Fraudsters increasingly target the fast-growing Internet of Things (IoT), where cyberattacks on intermediary communication media and IoT devices can go unnoticed and severely disrupt services, drive financial loss, and threaten privacy. Dependable IoT services require real-time intrusion detection, yet conventional IDS rely on fixed signatures or criteria and may miss complex or novel attacks. This work improves detection using machine learning, feature selection, RTGBO for feature relevance, and ELM for traffic classification, achieving 97–98% accuracy across multiple attack types.","Optimized Intrusion Detection for IoT Networks Using Machine Learning and Feature Selection with  \nRTGBO-ELM Integration  \nChandrakanth Reddy Borra  \nGraduate Student, University of the Cumberlands, United States  \n[chandu126099@gmail.com](chandu126099@gmail.com)  \nZabiha Khan  \nDepartment of AIMLNitte Meenakshi Institute of Technology, Bengaluru  \n[zabiha.khan@nmit.ac.in](zabiha.khan@nmit.ac.in)  \nRamya Vani Rayala  \nGraduate Student, University of the Cumberlands, United States  \n[ramyavanirayala@gmail.com](ramyavanirayala@gmail.com)  \nSrinivas Cheekati  \nGraduate Student, University of the Cumberlands, United States  \n[scheekati10826@gmail.com](scheekati10826@gmail.com)  \nAbstract  \nMore than ever before, fraudsters are fixated on the Internet of Things (IoT) because to its astounding rate of growth. The assertion is supported by the cumulative frequency of cyberattacks targeting intermediary communication media and IoT devices. If attacks on the IoT go undiscovered for a longtime, they disrupt services severely, which costs money. Additionally, it poses a risk to one's privacy. For IoT-enabled services to be dependable, safe, and lucrative, real-time intrusion detection on IoT devices is crucial. Conventional intrusion detection systems (IDSs) look for common threats using predetermined signatures or criteria, but they could miss more complex or unique attacks. One way to make intrusion detection systems better at detecting attacks is to incorporate ML and DL algorithms into them. Overall, this will strengthen cybersecurity and make it more resilient. Overfitting and the influence of irrelevant characteristics on the discovery of significant patterns are two of the many challenges that ML and DL approaches confront, which can have an influence on the efficacy and performance of the models. Optimising the machine learning models used by intrusion detection systems (IDSs) is necessary to guarantee improved performance and dependability when confronted with novel and unexpected threats. Resolving the issue of overfitting and incorporating feature selection can accomplish this. Here, to present a method for improving intrusion detection in the IoT by preprocessing with machine learning and feature selection. Ring-Toss-GameBased Optimisation (RTGBO) is used to optimally choose the most relevant features, and the Extreme Learning Machine (ELM) model is used to classify the network traffic. During the experimental presentation analysis, the advocated system shows dependable performance for both simulated and real invasions. It has an average detection accuracy of 97 to 98% for Blackhole, Distributed Denial of Service, Opportunistic Service, Sinkhole, and Wormhole assaults.  \nKeywords: Extreme Learning Machine; Intrusion detection schemes; Internet of Things; Ring-Toss-Game-Based Optimization; Distributed Denial of Service; Blackhole.  \nIntroduction  \nThe term IoT refers to a network of interconnected computing devices, software, and physical items that may collect and transmit data wirelessly. Because of these  \ncharacteristics, they can receive, send, and gather data [1] . Common uses for this data include controlling and observing the physical world. Information gathered via these more extensive network architectures may endanger physical  \nsafety, violate data privacy, and damage data integrity [2] . DDoS attacks, botnets, malware infestations, and ransomware are just some of the cyber hazards that may be easily launched with these tactics [3] . In order to reduce the likelihood of intrusion attacks on IoT systems, it is crucial to protect the IoT infrastructure from any dangers. The use of IDSs can achieve this goal [4] . An IDS acts as a digital companion for networks. It kept a close eye out for anything out of the ordinary and notified the admins if it detected anything fishy. Network security cannot be assured without an advanced IDS that can detect both existing and newly discovered threats [5] .  \nTraditional intrusion ","cbCaihiXnHpf2KHa","https://ap.wps.com/l/cbCaihiXnHpf2KHa","pdf",235020,7,1,6,"English","en",105,"# Abstract\n# Introduction\n## Background and IoT security risks\n## Limitations of traditional IDS\n## Role of ML and DL in intrusion detection","[{\"question\":\"Why is real-time intrusion detection important for IoT networks?\",\"answer\":\"IoT services rely on continuous communication, and undetected intrusions can severely disrupt services, cause financial loss, and threaten privacy. Real-time IDS helps identify malicious activity promptly.\"},{\"question\":\"What limitations do traditional intrusion detection systems have?\",\"answer\":\"Traditional IDS often depend on signatures or anomaly rules that require frequent updates, can miss new or modified attacks, and can generate many false positives and alerts. This reduces accuracy and may cause analysts to overlook real threats.\"},{\"question\":\"How do RTGBO and ELM contribute to the proposed intrusion detection approach?\",\"answer\":\"RTGBO is used to optimally select the most relevant features to reduce the impact of irrelevant characteristics and overfitting. ELM classifies network traffic to distinguish attacks from normal behavior.\"}]",1784236222,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"optimized-intrusion-detection-for-iot-networks-using-machine-learning-and-feature-selection-with-rtgbo-elm-integration","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"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/optimized-intrusion-detection-for-iot-networks-using-machine-learning-and-feature-selection-with-rtgbo-elm-integration/86616/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-07-29","2026-07-16",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is real-time intrusion detection important for IoT networks?","Question",{"text":76,"@type":77},"IoT services rely on continuous communication, and undetected intrusions can severely disrupt services, cause financial loss, and threaten privacy. Real-time IDS helps identify malicious activity promptly.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What limitations do traditional intrusion detection systems have?",{"text":81,"@type":77},"Traditional IDS often depend on signatures or anomaly rules that require frequent updates, can miss new or modified attacks, and can generate many false positives and alerts. This reduces accuracy and may cause analysts to overlook real threats.",{"name":83,"@type":74,"acceptedAnswer":84},"How do RTGBO and ELM contribute to the proposed intrusion detection approach?",{"text":85,"@type":77},"RTGBO is used to optimally select the most relevant features to reduce the impact of irrelevant characteristics and overfitting. ELM classifies network traffic to distinguish attacks from normal behavior.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,115,119,122,127,130,134],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":22,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},"Technology",50,"technology",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"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":107,"slug":137},19,"General","general"]