[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127689-en":3,"doc-seo-127689-105":29,"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":20,"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},127689,962084925782,"Ava Thompson","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Optimization Algorithms with Machine Learning to Improve Security of Internet of Things - Hybrid Optimization with Genetic and PSO","Internet of Things (IoT) deployments rely on continuous network traffic exchange, making traffic classification a key step for analyzing activities and supporting security decisions. Prior machine learning approaches have faced limits in feature extraction and accuracy, motivating a more effective pipeline. The paper proposes a hybrid optimization method combining genetic algorithms and PSO to extract discriminative features, then applying Random Forest for classification, validated with Python-based experiments using metrics such as accuracy, precision, and recall.","Optimization Algorithms with Machine Learning to Improve Security of Internet of Things  \nZakiya Manzoor Khan  \nDepartment of Computer Science and Engineering  \nLovely Professional University  \nPhagwara, Jalandhar, Punjab  \n[zakiyamanzoorkhan@gmail.com](zakiyamanzoorkhan@gmail.com)  \nHarjit Singh  \nAssociate Professor and Assistant Dean– Department of Computer Science and Engineering  \nLovely Professional University  \nPhagwara, Jalandhar, Punjab  \n[harjit.14952@lpu.co.in](harjit.14952@lpu.co.in)  \nAbstract—The IOT network traffic classification is the approach which helps to analyse IOT network traffic. The network traffic analysis can to various network activities. The network traffic analysis process has various steps which include data input, pre-processing, feature extraction, classification and performance analysis. The various machine learning algorithms is proposed in the previous years but those algorithms are unable to achieve high accuracy. The algorithms which are already proposed is unable to extract features from the dataset. To propose algorithm which can extract features from the dataset and achieve high accuracy for the network traffic classification is the motivation this research work. To achieve high accuracy hybrid optimization algorithm is proposed in this paper which is the combination of genetic and PSO algorithm. The hybrid optimization algorithm extract features and later it will be classified using Random Forest. The proposed model is implemented in python and results is achieved in terms of accuracy, precision, recall.  \nKeywords-IOT, PSO, Genetic, Random Forest  \nI. INTRODUCTION  \nIn the contemporary world, an extensive network known as the Internet of Things (IoT) connects billions of devices, facilitating communication between them. Coined by Kevin Ashton in 1999 during his work on supply chain optimization at Proctor & Gamble, the term has evolved over two decades, encompassing diverse applications in fields like healthcare, agriculture, utilities [1], and transportation. Despite this evolution, the fundamental aim of IoT remains consistent: enhancing efficiency and delivering information swiftly without relying solely on human interactions. Over the past five years, IoT has experienced significant growth. Projections suggest that the number of IoT devices will surge to 38.6 billion in 2025 and reach 50 billion by 2030. These devices continually gather various data from users, including browsing history, location, contacts, calendar events, and health records [2] . The primary motivations behind collecting such sensitive data are convenience and the enhancement of efficiency through smart device usage. As devices become more intelligent, they adeptly respond to daily needs, such as automatically adjusting lights at specific times,  \nhandling emergencies like fires, or addressing security concerns through advanced security systems. However, the daily convenience offered by these devices also introduces significant security risks.  \nSmart devices within the Internet of Things (IoT) network store highly personalized and private information [3] . Unauthorized access to such data by individuals or agents can lead to substantial harm to the user's well-being and safety. For instance, hackers could seize control of self-driving vehicles, potentially causing severe harm to the driver, or infiltrate home security cameras, violating privacy. The diverse range of IoT devices introduces security and privacy challenges. Without a secure system enabling these devices to exchange information privately, new IoT devices may fail to meet user expectations, discouraging users if their personal data cannot be adequately safeguarded. IoT networks present unique challenges, including privacy concerns, authentication issues, storage limitations, and data processing speeds [4]. Additionally, IoT devices themselves often lack essential security modules and software, creating vulnerabilities that cyber attackers can explo","cbCaiiZI6rPrv0Rb","https://ap.wps.com/l/cbCaiiZI6rPrv0Rb","pdf",439630,1,"English","en",105,"# Introduction\n## IoT growth and security motivations\n## IoT architecture and security layers\n## Classification of IoT attacks by layer\n## Physical layer attacks","[{\"question\":\"Why is network traffic classification important for IoT security?\",\"answer\":\"Network traffic classification analyzes IoT network activities, enabling security-related assessment based on how traffic is processed and categorized.\"},{\"question\":\"What limitation do earlier machine learning algorithms face in this work?\",\"answer\":\"Earlier algorithms are reported to achieve insufficient accuracy and to be unable to extract effective features from the dataset.\"},{\"question\":\"How does the proposed method improve classification performance?\",\"answer\":\"A hybrid optimization algorithm combining genetic algorithms and PSO extracts features from the dataset, and Random Forest performs the final classification, evaluated with accuracy, precision, and recall.\"}]","Optimization Algorithms with Machine Learning to Improve Security of Internet of Things - 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