[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126775-en":3,"doc-seo-126775-105":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},126775,962084926284,"Aurora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Enhancing Cyber Security through Machine Learning-Based Anomaly Detection in IoT Networks - Research Overview","The rapid proliferation of IoT networks delivers transformative benefits while introducing serious cybersecurity risks that demand advanced anomaly detection. This research proposes a machine learning-driven framework for IoT, using One-Class SVM and Isolation Forest. One-Class SVM learns the normal data distribution to separate anomalies, while Isolation Forest isolates outliers by building isolation trees. The combined strategy supports real-time anomaly detection on IoT data streams, enabling timely responses to emerging threats. Key steps include data collection, preprocessing, and model training to strengthen IoT ecosystem protection, data integrity, and privacy.","Enhancing Cyber Security through Machine Learning-Based Anomaly Detection in IoT Networks  \nDr. Shreyas J1, Dr. Sudhakar K2, Dr. I. Bhuvaneshwarri3, Thamaraiselvan B4, Lakshmi.M5, Dr Pooja Nayak S6  \n1Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Karnataka 576104  \n[shreyas.j@manipal.edu](shreyas.j@manipal.edu)  \n2B.N.M. Institute of Technology, Bengaluru  \n[ksudhakar.cs@bnmit.in](ksudhakar.cs@bnmit.in)  \n3Government College of Engineering, Erode  \n[ibw@gcee.ac.in](ibw@gcee.ac.in)  \n4K S Rangasamy Institute of Technology, Tiruchengode  \n[thamarai19799@gmail.com](thamarai19799@gmail.com)  \n5Nitte Meenakshi Institute of Technology, Bangalore  \n[lakshmi.m@nmit.ac.in](lakshmi.m@nmit.ac.in)  \n6Dayananda Sagar Academy of Technology and Management  \n[Pooja-ise@dsatm.edu.in](Pooja-ise@dsatm.edu.in)  \nAbstract: The rapid proliferation of IOT (Internet of Things) networks has brought transformative benefits to industries and everyday life. However, it has also introduced unprecedented cyber security challenges, necessitating advanced techniques for anomaly detection. This research focuses on enhancing cyber security through the application of machine learning-based anomaly detection methods, specifically OneClass Support Vector Machine (SVM) and Isolation Forest, in the context ofIOT networks. While Isolation Forest effectively isolates anomalies by building isolation trees, One-Class SVM models the normal data distribution, effectively separating anomalies. To provide a strong security framework for IoT networks, we suggest a comprehensive strategy that combines both algorithms. Our method enables the detection of anomalies in real-time IOT data streams, facilitating prompt responses to new threats. Data collection, preprocessing, and model training are key components. This study helps protect IOT ecosystems and maintain data integrity and privacy in an increasingly connected world by utilizing the benefits of One-Class SVM and Isolation Forest.  \nKeywords: Machine Learning, Anomaly Detection ,Cyber Security, One Class SVM, Isolation Forest , Network Security.  \nI. Introduction:  \nThe internet of things, or IoT, is a system of interconnected devices that connect to one another and share data with the cloud and other IoT devices. IoT simply describes how common objects and devices can gather, exchange, and process data without the need for human intervention by being internet-connected. Various opportunities have been made possible by this interconnection, including the development of smart homes and cities as well as industrial automation and improvements in medicine. However, as IoT spreads further, it has also revealed a number of important security issues that require our attention. IoT includes a huge ecosystem of gadgets, from tiny sensors and actuators to substantial industrial machinery and bright appliances. These devices have sensors, communication components, and computing abilities that enable them to collect information, interact with other devices, and make independent choices. This information can be used for a variety of things, including boosting convenience, increasing efficiency, and enabling insights based on data.  \nIOT Security challenges:  \nThe rapid proliferation of IoT devices, which expands the attack surface, the resource constraints of many IoT devices, the lack of adequate authentication mechanisms that expose devices to unauthorized access, the collection and transmission of sensitive data, concerns over data privacy, the incompatibility of devices from various manufacturers, and communication problems are just a few of the challenges that IoT cyber security must overcome.  \nDevice Proliferation: Cybercriminals have a huge attack surface thanks to the IoT devices quick proliferation. Each of these gadgets, which can be anything from straightforward sensors to intricate industrial machinery, represents a potential point of entry into a network. The task of securing the numer","cbCaibvwcuYcrCTS","https://ap.wps.com/l/cbCaibvwcuYcrCTS","pdf",308962,1,6,"English","en",105,"# Introduction\n## IOT Security challenges\n## Device Proliferation\n## Limited Resources\n## Firmware Updates\n## Physical Security\n## Vulnerabilities in the supply chain\n## Legacy Hardware\n## Regulatory Compliance\n# Literature review","[{\"question\":\"What problem does the research address for IoT networks?\",\"answer\":\"It addresses the cybersecurity challenges caused by the rapid growth of IoT, which expands attack surfaces and increases risks that require advanced anomaly detection.\"},{\"question\":\"Which two machine learning methods are used for anomaly detection?\",\"answer\":\"The study uses One-Class SVM and Isolation Forest to detect anomalies in IoT data streams.\"},{\"question\":\"How do One-Class SVM and Isolation Forest differ in detecting anomalies?\",\"answer\":\"Isolation Forest isolates anomalies by constructing isolation trees, while One-Class SVM models the normal data distribution and separates anomalies from it.\"}]","Enhancing Cyber Security through Machine Learning-Based Anomaly Detection in IoT Networks - 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