Botnet Attack Detection in the Internet of Things Using Selected Learning Algorithms

<p><strong>A Must-Read for IoT Security Researchers and Machine Learning Experts</strong></p><p>As IoT networks continue to expand so do the complexities of securing them against <strong>botnet attacks</strong>. The diversity of devices varying computational capabilities and different communication protocols make developing a <strong>universal botnet detection system</strong> a significant research challenge. This book provides a <strong>rigorous data-driven approach</strong> to tackling this issue using <strong>supervised machine learning algorithms</strong>.</p><p>Based on the <strong>NB-IoT-23 dataset</strong> this research evaluates multiple classification techniques including <strong>Logistic Regression Linear Regression Artificial Neural Networks (ANN) K-Nearest Neighbors (KNN) and Bagging</strong>. The findings reveal that the <strong>Bagging ensemble model</strong> outperforms others achieving an exceptional <strong>99.96% accuracy</strong> with minimal computational overhead making it a strong candidate for <strong>real-world IoT botnet detection systems</strong>.</p><h3><strong>Key Features for Academic Researchers:</strong></h3><ul><li><strong>Comprehensive IoT Security Analysis</strong> - Explore the unique challenges of botnet detection across diverse IoT devices.</li><li><strong>Advanced Machine Learning Techniques</strong> - Compare different learning algorithms and their effectiveness in botnet detection.</li><li><strong>High-Quality Dataset & Empirical Evaluation</strong> - Gain insights from <strong>real-world NB-IoT-23 datasets</strong> featuring data from multiple IoT devices.</li><li><strong>Research-Backed Findings</strong> - The book presents reproducible results making it a <strong>valuable reference for Master's and Ph.D. students</strong> exploring IoT security cybersecurity and machine learning.</li><li><strong>Future Research Directions</strong> - Identify gaps and opportunities for further exploration in <strong>IoT security and anomaly detection</strong>.</li></ul><p>This book serves as a <strong>practical and theoretical resource</strong> for graduate students cybersecurity professionals and researchers interested in <strong>IoT security network intrusion detection and applied machine learning</strong>.</p><p><strong>Enhance your research and contribute to securing IoT networks-get your copy today!</strong></p>