[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121285-en":3,"doc-seo-121285-105":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":4,"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},121285,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Attack and Anomaly Detection in IoT Sensors Using Machine Learning Approaches","Widespread deployment of IoT sensors improves data collection and monitoring across domains like smart agriculture and industrial automation, but it also increases exposure to attacks and anomalous behavior. This paper investigates machine learning approaches suitable for protecting IoT sensor networks under limited device resources, where conventional security measures are insufficient. Using a dataset from simulated IoT environments, the work covers data preprocessing, exploratory analysis, feature engineering, and training/testing of Logistic Regression, Decision Tree, and Random Forest models to distinguish normal from anomalous patterns.","Attack and Anomaly Detection in IoT Sensors Using Machine Learning Approaches  \nAshish Seth   \nProfessor, School of Computer and Information Engineering (SOCIE) Inha University in Tashkent,  \nUzbekistan  \n*Corresponding Author Email: [a.seth@inha.uz](a.seth@inha.uz)  \n This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly  \ncited.  \nAbstract  \nThe extensive usage of IoT sensors significantly improved the collection and monitoring of data within various application domains, such as smart agriculture and industrial automation. On the other hand, the great dependence on IoT sensors makes systems vulnerable to hacks and anomalies. In this paper, we explore machine learning approaches that can be used to protect Internet of Things sensor networks against attacks and anomalies. Due to the limited resources available to IoT devices, traditional security measures fall short. There is, therefore, a need to develop more intelligent smart detection systems. This paper examines the capabilities of machine learning in identifying patterns of anomalies in IoT sensor data. Carried out on a dataset of simulated IoT environments, the research presents the stages of data preprocessing, exploratory data analysis, and feature engineering. In addition, three models; Logistic Regression, Decision Tree, and Random Forest were constructed and tested. The results show that it is possible to use machine learning algorithms for anomaly detection in IoT domains, thereby presenting the possibilities for improving IoT security and reliability. The findings ofthis study are important in that they highlight how advanced analytics can help organizations deal with IoT environments.  \nKeywords: Internet of Things, Cybersecurity, Machine Learning, Sensor Networks.  \n1. INTRODUCTION  \nThe exponential growth of IoT devices in recent years has led to the emergence of big data in the form of sensor data, which creates problems with data processing and protection. IoT is creating opportunities to improve and innovate many organizations’ operations and their customers’ experiences, however, it also brings the likelihood of detection of alterations in this data is necessary so that the IoT systems can maintain security, credibility, and accuracy [1] . This research is centered on using machine learning approaches to detect any anomalous trends within the IoT sensor data set from a simulated IoT system environment. This dataset includes different IoT devices and services such as light controllers, thermostats, and smart doors within one day. Three models of machine learning namely Logistic Regression, Decision Tree, and Random Forest have been used in the research to assess their performance in terms of anomaly detection [10] . In those models, every model is then evaluated based on its capacity to distinguish normal from anomalous behavior. This assignment focuses on selecting the best way to detect anomalies in IoT systems by going through processes that involve data preprocessing, feature extraction, and model assessment. This study prepares a differential analysis of these models’ performance to inform the utilitarian values of machine learning in improving IoT security and functionality [11][12] .  \n2. LITERATURE SURVEY  \nAnomaly Detection in IoT Systems  \nAccording to Abusitta, et al. 2023 [1], anomaly detection in the IoT context becomes challenging and important as a large number of We Smart devices are producing great volumes of data. Static methods that are normally used in anomaly detection fail to offer an optimal solution since they are based on statistical or rule-based models and are rigid when it comes to handling different data from the IoT environment. Later developments are based on the usage of machine learning approaches to improve the accuracy of the detection. Advan","cbCaifSrSwfFXlzm","https://ap.wps.com/l/cbCaifSrSwfFXlzm","pdf",1080340,1,12,"English","en",105,"# Abstract\n# 1. Introduction\n# 2. Literature Survey\n## Anomaly Detection in IoT Systems\n## Machine Learning Models for Anomaly Detection\n## Feature Engineering for Anomaly Detection","[{\"question\":\"Why is anomaly detection important in IoT sensor networks?\",\"answer\":\"IoT systems become vulnerable to attacks and anomalous trends due to heavy dependence on sensor data. Detecting alterations is necessary to maintain security, credibility, and accuracy.\"},{\"question\":\"Which machine learning models are used for anomaly detection?\",\"answer\":\"The paper constructs and tests Logistic Regression, Decision Tree, and Random Forest models, evaluating how well each distinguishes normal from anomalous behavior.\"},{\"question\":\"What role does feature engineering play in the proposed approach?\",\"answer\":\"Sensor data is high-dimensional and noisy, so preprocessing and mapping via feature engineering—including normalization, encoding categorical data, and handling missing values—improves anomaly detection effectiveness.\"}]","Attack and Anomaly Detection in IoT Sensors Using Machine Learning Approaches | PDF",1785734900,30,{"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},"attack-and-anomaly-detection-in-iot-sensors-using-machine-learning-approaches","",{"@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/attack-and-anomaly-detection-in-iot-sensors-using-machine-learning-approaches/121285/",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":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is anomaly detection important in IoT sensor networks?","Question",{"text":75,"@type":76},"IoT systems become vulnerable to attacks and anomalous trends due to heavy dependence on sensor data. Detecting alterations is necessary to maintain security, credibility, and accuracy.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are used for anomaly detection?",{"text":80,"@type":76},"The paper constructs and tests Logistic Regression, Decision Tree, and Random Forest models, evaluating how well each distinguishes normal from anomalous behavior.",{"name":82,"@type":73,"acceptedAnswer":83},"What role does feature engineering play in the proposed approach?",{"text":84,"@type":76},"Sensor data is high-dimensional and noisy, so preprocessing and mapping via feature engineering—including normalization, encoding categorical data, and handling missing values—improves anomaly detection effectiveness.","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},[93,97,101,105,110,115,120,122,127,130,134],{"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":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"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"]