Conventional methodologies employed in detecting distributed denial-of-service attacks have frequently struggled to adapt to the dynamic and multi-faceted evolution of such threats. Furthermore, many of the contempora...
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Conventional methodologies employed in detecting distributed denial-of-service attacks have frequently struggled to adapt to the dynamic and multi-faceted evolution of such threats. Furthermore, many of the contemporary detection and prevention solutions, while innovative, remain anchored to dedicated workstations, lacking the flexibility and scalability required in today's digital landscape. To bridge this technological chasm, this research introduces a state-of-the-art intrusion detection system firmly rooted in advanced Deep Learning techniques. By leveraging the expansive and adaptable nature of cloud-centric, service-oriented architectures, we not only bolster detection precision but also offer a solution designed for modern infrastructures. This system provides enterprises with a robust, easily deployable tool that is both versatile in its application and proactive in its defence approach, ensuring that networks remain resilient against the continuously evolving spectrum of cyber threats.
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