[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118219-en":3,"doc-seo-118219-105":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},118219,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Enhancing IoT Security through Machine Learning-Driven Anomaly Detection - read online free","The study addresses escalating cybersecurity challenges driven by the rapid adoption of Internet of Things (IoT) devices, where vulnerabilities and evolving attack vectors create complex security conditions. It develops a machine learning-based anomaly detection approach combined with adaptive defense mechanisms to counter hidden threats. The work outlines data sources and preprocessing steps and evaluates models including Random Forest, Decision Tree, SVM, and Gradient Boosting for detecting abnormal patterns. Results and discussion assess accuracy, precision, and recall, highlighting Gradient Boosting achieving 89.34% precision. The research concludes that machine learning enables a robust, adaptive system for securing IoT against ever-emerging cyber-attacks.","Keywords: IOT, Machine Learning, Deep Learning, IOT Anomaly Detection.  \nJournal Info:  \nSubmitted: February 20, 2024 Accepted:  \nApril 26, 2024  \nPublished: May 16, 2024  \nEnhancing IoT Security through Machine Learning-Driven Anomaly Detection  \nUsama Tahir 1* , Muhammad Kamran Abid1 , Muhammad fuzail1 , Naeem aslam1  \n1 NFC Institute of Engineering and Technology, Multan, Pakistan  \nAbstract  \nThis is study emphasizes the growing cybersecurity situations arising from the increasing use of Internet of Things (IoT) devices. Paying the main attention to the development of IoT security, the work here deploys the machine learning-based anomaly detection and adaptive defense mechanisms as proactive methods to counteract existing plus future cyber threat sources. The visual serves to expound the rapid development of the Internet of Things, and it also highlights the importance of infrastructures with robust safety features to secure the connected devices. IoT security statement brings out the hidden threat and vulnerabilities of the IoT, in this context advanced security measures are for the rescue. The objectives concentrate on improving security of IoT via machine learning detection of anomalies, and bring introduction of defense mechanisms that are adaptive. We specify the data sources, preprocessing tasks, and Random Forest, Decision Tree, SVM, and Gradient Boosting algorithms selected for anomaly detection in the methodology section. The abnormity negotiation function and the self-adaptive defense procedures are combined in order to strengthen the information technology ecosystems which are capable of dynamic simplification. The results and discussion part hotelatesthe effectiveness of machine learning models selected, and indicates about accuracy, precision, and recall metrics. To state in the most significant matter, Gradient Boosting brings the greater precision of 89.34% . Table 3 below indicates the various models’ effectiveness. It is proven that Gradient Boosting is the most powerful model among all. The discourse unfolds with account of the results, acknowledgment of the limitations, and discussion crucial obstacles encountered in the realization of the research. The conclusion reaffirms the importance of machine learning in IoT security implementation, thus building a robust system that can evolve to fight the ever-emerging cyber-attacks, keeping up with the progressive direction for securing IoT through the connected world.  \n*Correspondence author email address: [usama007tahir@gmail.com](usama007tahir@gmail.com)  \nDOI: 10.21015/vtse.v12i2 .1766  \nThis work is licensed under a Creative Commons Attribution 3.0 License.  \nVFAST Transactions on Software Engineering Volume 12, Issue 2, 2024  \n1 Introduction  \nThe power of the Internet of Things (IoT) technology enabling IoT devices to communicate and share data unquestionably has hada pronounced impact on how we navigate through our daily lives. Nevertheless, this upsurge of cyber threats has twofold negative outcome – a complicated security environment and the presence of vulnerabilities.  \nParticularly in these circumstances, therefore, this study aims to solve the acute problem of the enhancement of IoT safety with the help of Machine Learning application. Attack prevention and cybersecurity reinforcement via anomaly detection and adaptive defenses will be among our critical areas of focus in the quest to turn the IoT ecosystem into a strong infrastructure impenetrable of emerging cyber threats.  \nMachine learning (ML) and deep learning (DL) are becoming indispensable techniques for resisting security risks as the Internet of Things (IoT) grows in popularity.  \nThis paper investigates the inner workings of various machine learning techniques, such as Convolutional Neural Networks (CNNs), Random Forests, and Support Vector Machines (SVMs) . In the context of the Internet of Things, anomaly detection refers to the identification of unusual patterns or behaviorsin data that d","cbCaiiiBklmSFnYu","https://ap.wps.com/l/cbCaiiiBklmSFnYu","pdf",231584,1,13,"English","en",105,"# Introduction\n## Problem Statement\n# Machine Learning for Anomaly Detection\n## ML/DL Techniques and Models\n# Methodology\n## Data Sources and Preprocessing\n## Classification Algorithms\n# Results and Discussion\n## Accuracy, Precision, Recall\n# Conclusion","[{\"question\":\"What security problem does the study focus on?\",\"answer\":\"The study focuses on improving IoT safety by strengthening cybersecurity through anomaly detection and adaptive defenses against current and future cyber threats.\"},{\"question\":\"Which machine learning algorithms are used for anomaly detection?\",\"answer\":\"The methodology evaluates Random Forest, Decision Tree, SVM, and Gradient Boosting, selected for anomaly detection.\"},{\"question\":\"Why is Gradient Boosting emphasized in the results?\",\"answer\":\"Gradient Boosting is reported to deliver the strongest effectiveness, with higher precision reaching 89.34%, outperforming the other evaluated models.\"}]","Enhancing IoT Security through Machine Learning-Driven Anomaly Detection - 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