<![CDATA[Cyber attacks, particularly Distributed Denial of Service (DDoS), have become a serious threat to the availability of servers and other network infrastructure. These attacks can paralyze services on large-scale networks by flooding the target system with extremely high traffic. Based on this, the objective of this research is to simulate and analyze a Hybrid Teaming strategy using the Naïve Bayes algorithm. This strategy simulates structured collaboration between the Red Team (attackers), Blue Team (defenders), and Purple Team (evaluators) to test resilience while comprehensively strengthening the security posture. The Naïve Bayes algorithm is one of the best algorithms in Machine Learning and excels at performing data classification processes. The performance of the Naïve Bayes algorithm combined with the Hybrid Teaming strategy is developed into an intelligent detection system. This system is trained using 10,000 data points from a public dataset and 1,688 data points from the network logs of the Tapan Regional General Hospital (RSUD). Based on the data analysis results, the model training outcomes fall into the perfect category, with accuracy, precision, recall, and F1-score achieving a result of 100%. The model was then implemented on a server and a MikroTik router within a simulation environment that replicates the Tapan RSUD network. The test results on these two components show that the system successfully detected various Flooding attack patterns with a detection accuracy of 100%. The system is capable of responding automatically by blocking the attacker's IP (Internet Protocol) address at both layers, as well as sending real-time notifications via WhatsApp and Email. The contribution of this research results in a comprehensive and effective cybersecurity defense framework.]]>
