[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124098-en":3,"doc-seo-124098-105":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},124098,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","A secure framework for the Internet of Things anomalies using machine learning","The Internet of Things (IoT) revolutionises modern technology through connectivity and automation, yet widespread adoption increases attack surface and security vulnerabilities. This paper proposes a secure IoT anomaly detection framework that leverages four machine learning algorithms: Logistic Regression (LR), Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), and Gaussian Naive Bayes (GNB). Synthetic datasets with induced anomalies are analyzed using AWS IoT Core infrastructure and Python to identify abnormal device behaviour. Experiments report 91–98% detection accuracy, with CART achieving the strongest results. Precision, recall and F1-score validate reliable separation between normal and anomalous IoT data. The approach enables scalable monitoring, proactive detection, and improved mitigation for connected environments, supporting more trustworthy and efficient IoT systems.","Research  \nA secure framework for the Internet of Things anomalies using machine learning  \nVijay Prakash1 · Olukayode Odedina2 · Ajay Kumar3 · Lalit Garg1 · Seema Bawa4  \nReceived: 4 October 2024 / Accepted: 13 December 2024  \n© The Author(s) 2024 OPEN  \nAbstract  \nThe Internet of Things (IoT) revolutionises modern technology, offering unprecedented opportunities for connectivity and automation. However, the increased adoption of IoT devices introduces substantial security vulnerabilities, necessitating effective anomaly detection frameworks. This Paper proposes a secure IoT anomaly detection framework by utilising four machine learning algorithms such as: Logistic Regression (LR), Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), and Gaussian Naive Bayes (GNB). By generating synthetic datasets with induced anomalies, the framework employs AWS IoT Core infrastructure and Python-based analysis to identify irregularities in device performance. The proposed framework achieved a high detection accuracy ranging from 91 to 98% across the tested algorithms, with CART showing the best performance. Key performance metrics, including precision, recall, and F1-score, confirmed the model’s reliability in distinguishing between normal and anomalous IoT data. Experimental results demonstrate superior detection accuracy across all methods, validating the robustness of the proposed approach. This research offers a scalable solution for IoT security, paving the way for improved anomaly detection and mitigation strategies in connected environments. The integration of machine learning algorithms with IoT infrastructure allows for real-time monitoring and proactive anomaly detection in diverse IoT applications. The proposed framework enhances security measures and contributes to the overall reliability and efficiency of connected systems.  \nArticle Highlights  \n• A secure IoT framework uses ML models (LR, LDA, CART, GNB) to detect anomalies in sensor data.  \n• IoT security solution outperforms existing methods with an anomaly detection accuracy of 91–98% .  \n• The CART algorithm showed superior performance in anomaly detection among tested models.  \nKeywords Internet of Things · Machine learning · Classification models · Anomalies · Security framework · Ensembling  \n* Vijay Prakash, [vijaysoni200@gmail.com](vijaysoni200@gmail.com); Olukayode Odedina, [olukayode.odedina@online.liverpool.ac.uk](olukayode.odedina@online.liverpool.ac.uk); Ajay Kumar, ajaycpp@  \n[gmail.com](gmail.com); Lalit Garg, [lalit.garg@um.edu.mt](lalit.garg@um.edu.mt); Seema Bawa, [seema@thapar.edu |](seema@thapar.edu |1University of Malta)[1](seema@thapar.edu |1University of Malta)[University of Malta](seema@thapar.edu |1University of Malta), Msida, Malta. 2University of Liverpool,  \nLiverpool, UK. 3Faculty of Information Technology and Engineering, Gopal Narayan Singh University, Sasaram, Bihar, India. 4Thapar Institute of Engineering & Technology, Patiala, Punjab, India.  \nDiscover Internet of Things  \n(2024) 4:33  \n| [https://doi.org/10.1007/s43926-024-00088-z](https://doi.org/10.1007/s43926-024-00088-z)  \n1 Introduction  \nAn anomaly is an unusual or unexpected behaviour that differs from the actual or expected behaviour. These are some patterns in the dataset that deviate from their general behaviour. Anomaly detection is a technique for finding the dataset’s abnormal data pattern. Several names for different application domains refer to anomalies, such as outliers, exceptions, surprises, unexpected observations, and peculiarities. Anomaly detection is used in various applications like credit-card fraud [1–3], intrusion detection systems in computer networks [4–8], fraud detection in health care [9, 10] and the insurance sector [11, 12], wireless banking sensors and social networks, etc. [13]. Anomaly detection is crucial regarding the critical or significant, actionable information identified in various applications, like abnormal traffic pa","cbCaimE3yqgJLx7b","https://ap.wps.com/l/cbCaimE3yqgJLx7b","pdf",2407156,1,32,"English","en",105,"# Introduction\n## Anomaly detection concepts and applications\n## IoT background and security relevance","[{\"question\":\"Which machine learning algorithms are used in the proposed IoT anomaly detection framework?\",\"answer\":\"The framework uses Logistic Regression (LR), Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), and Gaussian Naive Bayes (GNB).\"},{\"question\":\"How does the framework generate data for detecting anomalies?\",\"answer\":\"It generates synthetic datasets with induced anomalies, then analyzes the data to identify irregularities in device performance using AWS IoT Core and Python-based processing.\"},{\"question\":\"What performance results does the paper report for anomaly detection accuracy?\",\"answer\":\"Detection accuracy ranges from 91% to 98% across the tested algorithms, and CART shows the best performance among them. Precision, recall, and F1-score further confirm model reliability.\"}]","A secure framework for the Internet of Things anomalies using machine learning | PDF",1785820306,81,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"a-secure-framework-for-the-internet-of-things-anomalies-using-machine-learning","",{"@graph":36,"@context":86},[37,54,69],{"@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/a-secure-framework-for-the-internet-of-things-anomalies-using-machine-learning/124098/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"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","2026-08-05","2026-08-04",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},"Which machine learning algorithms are used in the proposed IoT anomaly detection framework?","Question",{"text":76,"@type":77},"The framework uses Logistic Regression (LR), Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), and Gaussian Naive Bayes (GNB).","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the framework generate data for detecting anomalies?",{"text":81,"@type":77},"It generates synthetic datasets with induced anomalies, then analyzes the data to identify irregularities in device performance using AWS IoT Core and Python-based processing.",{"name":83,"@type":74,"acceptedAnswer":84},"What performance results does the paper report for anomaly detection accuracy?",{"text":85,"@type":77},"Detection accuracy ranges from 91% to 98% across the tested algorithms, and CART shows the best performance among them. 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