[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121146-en":3,"doc-seo-121146-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},121146,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Feature Reduction and Anomaly Detection in IoT Using Machine Learning Algorithms - Paper","Anomaly detection in IoT is addressed through a comparative study of multiple machine-learning methods under cybersecurity constraints and the rising scale of IoT data traffic. The work evaluates Naïve Bayesian, Support Vector Machine, Decision Tree, XGBoost, Random Forest, and K-nearest Neighbor classifiers, while also applying three feature reduction techniques: Principal Component Analysis, Particle Swarm Optimization, and Gray Wolf Optimizer. Experiments on the RT-IoT2022 dataset use Precision, Recall, F-measure, and accuracy to quantify performance, with SVM achieving about 99.99% accuracy.","Feature Reduction and Anomaly Detection in IoT Using Machine Learning Algorithms  \nAdel Hamdan 1, Muhannad Tahboush2, Mohammad Adawy3, Tariq Alwada’n4, Sameh Ghwanmeh5 Computer Science Dept., The World Islamic Sciences and Education University, Amman, Jordan 1 Information System and Network Dept., The World Islamic Sciences and Education University, Amman, Jordan2, 3 Network and Cybersecurity Dept., Teesside University, Middlesbrough, UK4  \nComputer Science Dept., American University in the Emirates, Dubai, UAE5  \nAbstract—Anomaly detection in IoT is a hot topic in cybersecurity. Also, there is no doubt that the increased volume of IoT trading technology increases the challenges it faces. This paper explores several machine-learning algorithms for IoT anomaly detection. The algorithms used are Naïve Bayesian (NB), Support Vector Machine (SVM), Decision Tree (DT), XGBoost, Random Forest (RF), and K-nearest Neighbor (K-NN). Besides that, this research uses three techniques for feature reduction (FR). The dataset used in this study is RT-IoT2022, which is considered a new dataset. Feature reduction methods used in this study are Principal Component Analysis (PCA), Particle Swarm Optimization (PSO), and Gray Wolf Optimizer (GWO). Several assessment metrics are applied, such as Precision (P), Recall(R), F-measures, and accuracy. The results demonstrate that most machine learning algorithms perform well in IoT anomaly detection. The best results are shown in SVM with approximately 99.99% accuracy.  \nKeywords—Machine learning; Internet of Things (IoT); anomaly detection; feature reduction; Naïve Bayesian (NB); Support Vector Machine (SVM); Decision Tree (DT); XGBoost; Random Forest (RF); K-Nearest Neighbor (K-NN)  \nI. INTRODUCTION  \nDetecting anomalies on the Internet of Things (IoT) is a major security issue that has been investigated and studied for centuries. The Internet of Things (IoT) involves several devices capable of processing, collecting, storing data, and communicating. The adoption of the IoT brought many innovations to industries, homes, and businesses, and undoubtedly, it has improved the quality of life.  \nRecently, the Internet of Things (IoT) has experienced quick growth in many specific applications. Also, IoT has become a driving force for the current technology revolution. IoT captures valuable data daily, allowing individuals or users to make critical decisions. There are many applications for IoT, such as healthcare, transportation, agriculture, and others. Also, there is no doubt that IoT devices have some limitations, such as CPU, memory, and low-energy storage. IoT devices comprise several interconnected sensors, actuators, and other devices [1],[2] . A lot of research expected tremendous growth in IoT. For example, cisco predicted an average of 75.3 billion linked devices by 2025 [3], [4] .  \nIoT devices are extremely vulnerable to cyber-security threats targeting integrity and availability, and it is necessary to prevent cyber-security accidents. Thus, a Network Intrusion  \nDetection System (NIDS) is needed. NIDS can detect any anomaly to protect the IoT network and the device. NIDS has the ability to monitor all traffic across the IoT network and acts as a first defense line. Also, NIDS can identify networks against intruders and suspicious activity. In addition, NIDS can examine and investigate the devices on the network [5],[6], [7] .  \nAnomaly recognition can be divided into three categories based on the function of the training data stated as follows [2],[3], [4] .:  \nSupervised Anomaly Detection: The normal and abnormal training datasets contain labeled cases. Thus, this methodology is about creating a predictive model for the abnormal and normal classes and then comparing both together.  \nSemi-supervised anomaly detection: The learning here involves only common cases of the class. Thus, anything that cannot be classified as usual is marked as abnormal.  \nUnsupervised anomaly detection: The training ","cbCaimkSCMEmiTkE","https://ap.wps.com/l/cbCaimkSCMEmiTkE","pdf",1039504,1,"English","en",105,"# I. INTRODUCTION\n## IoT security and need for anomaly detection\n## Anomaly detection categories\n## Proposed approach and contributions\n# II. RELATED WORK","[{\"question\":\"Which machine-learning algorithms are evaluated for IoT anomaly detection?\",\"answer\":\"The paper evaluates Naïve Bayesian (NB), Support Vector Machine (SVM), Decision Tree (DT), XGBoost, Random Forest (RF), and K-nearest Neighbor (K-NN).\"},{\"question\":\"What feature reduction techniques are used in the research?\",\"answer\":\"Three feature reduction methods are applied: Principal Component Analysis (PCA), Particle Swarm Optimization (PSO), and Gray Wolf Optimizer (GWO).\"},{\"question\":\"What dataset and metrics are used to measure the results?\",\"answer\":\"Experiments use the RT-IoT2022 dataset and evaluate Precision, Recall, F-measures, and accuracy, with SVM reporting approximately 99.99% accuracy.\"}]","Feature Reduction and Anomaly Detection in IoT Using Machine Learning Algorithms - Paper | PDF",1785734078,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"feature-reduction-and-anomaly-detection-in-iot-using-machine-learning-algorithms-paper","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/feature-reduction-and-anomaly-detection-in-iot-using-machine-learning-algorithms-paper/121146/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Which machine-learning algorithms are evaluated for IoT anomaly detection?","Question",{"text":74,"@type":75},"The paper evaluates Naïve Bayesian (NB), Support Vector Machine (SVM), Decision Tree (DT), XGBoost, Random Forest (RF), and K-nearest Neighbor (K-NN).","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What feature reduction techniques are used in the research?",{"text":79,"@type":75},"Three feature reduction methods are applied: Principal Component Analysis (PCA), Particle Swarm Optimization (PSO), and Gray Wolf Optimizer (GWO).",{"name":81,"@type":72,"acceptedAnswer":82},"What dataset and metrics are used to measure the results?",{"text":83,"@type":75},"Experiments use the RT-IoT2022 dataset and evaluate Precision, Recall, F-measures, and accuracy, with SVM reporting approximately 99.99% accuracy.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]