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Our proposed detection strategies are applied to real-world network attack scenarios, demonstrating their effectiveness in mitigating botnet threats. We evaluate the model using applicable security metrics and compare the results with existing detection methodologies to validate the approach. Our findings indicate that the suggested strategies improve detection accuracy by 12% over conventional methods. Additionally, we conduct network emulations using Mininet, simulating Mirai botnet infections. The results show that the true positive and true negative detection rates for a network modeled by the Mafia game framework reach 71% and 91%, respectively. 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