MITIGATING DENIAL OF SERVICE ATTACKS THROUGH MACHINE LEARNING BASED INTRUSION DETECTION

This research examines the application of twelve leading Machine Learning (ML) techniques, utilizing the Pycaret module, to effectively analyze Distributed Denial of Service (DDoS) attacks. The objective is to develop a highly efficient methodology that facilitates the rapid detection, mitigation, and prevention of such cyber-attacks by thoroughly analyzing incoming network traffic and packet patterns. In comparison to conventional ML models like Random Forest, Decision Tree, and Gradient Boosting, the study's findings highlight that Pycaret, especially when integrated with the XGBoost model, significantly improves the accuracy and speed of DDoS detection. This approach plays a crucial role in preserving the availability, integrity, and confidentiality of cloud services, thereby minimizing financial losses, reputational damage, and strengthening overall cybersecurity in today’s fast-paced digital world

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