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Cyber Security: An Intelligent Machine Learning Framework for Detecting Distributed Denial-of-Service Attacks in Internet of Things
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Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 2 Artikel!
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| The rapid proliferation of Internet of Things (IoT) devices has transformed modern digital ecosystems by enabling seamless connectivity and intelligent automation across various domains, including healthcare, transportation, smart cities, and industrial systems. However, the resource-constrained nature and heterogeneous architecture of IoT networks make them highly vulnerable to cyber threats, particularly Distributed Denial-of-Service (DDoS) attacks. These attacks can compromise network availability, disrupt critical services, and cause substantial economic and operational losses. Traditional signature-based intrusion detection approaches often struggle to identify evolving and sophisticated attack patterns, necessitating the development of intelligent and adaptive security solutions. This study proposes an intelligent machine learning framework for detecting DDoS attacks in IoT networks. The framework integrates data preprocessing, feature engineering, and supervised machine learning techniques to effectively distinguish malicious traffic from legitimate network activities. |
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i.d.R. innert 7-14 Tagen versandfertig
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