{"product_id":"botnet-attack-detection-in-the-internet-of-things-using-selected-learning-algorithms-a-research-study-on-securing-iot-against-cyber-threats-using-mac-paperback-1","title":"Botnet Attack Detection in the Internet of Things Using Selected Learning Algorithms: A Research Study on Securing IoT Against Cyber Threats Using Mac - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eBolakale Aremu\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eA Must-Read for IoT Security Researchers and Machine Learning Experts\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eAs IoT networks continue to expand, so do the complexities of securing them against \u003cstrong\u003ebotnet attacks\u003c\/strong\u003e. The diversity of devices, varying computational capabilities, and different communication protocols make developing a \u003cstrong\u003euniversal botnet detection system\u003c\/strong\u003e a significant research challenge. This book provides a \u003cstrong\u003erigorous, data-driven approach\u003c\/strong\u003e to tackling this issue using \u003cstrong\u003esupervised machine learning algorithms\u003c\/strong\u003e.\u003c\/p\u003e\u003cp\u003eBased on the \u003cstrong\u003eNB-IoT-23 dataset\u003c\/strong\u003e, this research evaluates multiple classification techniques, including \u003cstrong\u003eLogistic Regression, Linear Regression, Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), and Bagging\u003c\/strong\u003e. The findings reveal that the \u003cstrong\u003eBagging ensemble model\u003c\/strong\u003e outperforms others, achieving an exceptional \u003cstrong\u003e99.96% accuracy\u003c\/strong\u003e with minimal computational overhead, making it a strong candidate for \u003cstrong\u003ereal-world IoT botnet detection systems\u003c\/strong\u003e.\u003c\/p\u003e\u003cstrong\u003eKey Features for Academic Researchers: \u003c\/strong\u003e\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eComprehensive IoT Security Analysis\u003c\/strong\u003e - Explore the unique challenges of botnet detection across diverse IoT devices.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eAdvanced Machine Learning Techniques\u003c\/strong\u003e - Compare different learning algorithms and their effectiveness in botnet detection.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eHigh-Quality Dataset \u0026amp; Empirical Evaluation\u003c\/strong\u003e - Gain insights from \u003cstrong\u003ereal-world NB-IoT-23 datasets\u003c\/strong\u003e featuring data from multiple IoT devices.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eResearch-Backed Findings\u003c\/strong\u003e - The book presents reproducible results, making it a \u003cstrong\u003evaluable reference for Master's and Ph.D. students\u003c\/strong\u003e exploring IoT security, cybersecurity, and machine learning.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eFuture Research Directions\u003c\/strong\u003e - Identify gaps and opportunities for further exploration in \u003cstrong\u003eIoT security and anomaly detection\u003c\/strong\u003e.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eThis book serves as a \u003cstrong\u003epractical and theoretical resource\u003c\/strong\u003e for graduate students, cybersecurity professionals, and researchers interested in \u003cstrong\u003eIoT security, network intrusion detection, and applied machine learning\u003c\/strong\u003e.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eEnhance your research and contribute to securing IoT networks-get your copy today!\u003c\/strong\u003e\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 90\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.19 x 9 x 6 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e March 19, 2025\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":45674140532933,"sku":"9798349220203","price":32.38,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0757\/6718\/5605\/files\/7pcAxSxOmp9798349220203_5d511c77-0180-4795-836b-acd9c66f8023.webp?v=1769075777","url":"https:\/\/selloorium.com\/products\/botnet-attack-detection-in-the-internet-of-things-using-selected-learning-algorithms-a-research-study-on-securing-iot-against-cyber-threats-using-mac-paperback-1","provider":"Selloorium","version":"1.0","type":"link"}