The rise of sophisticated cyberattacks has fueled the adoption of machine learning (ML) techniques in intrusion detection systems; however, the emergence of adversarial examples poses a critical challenge, allowing malicious actors to bypass detection or generate false alerts. In response, this paper introduces a novel approach to adversarial purification in intrusion detection utilizing diffusion models, which have demonstrated promising capabilities in various domains. Our research is the first to explore the effectiveness of these models specifically within the context of network intrusion detection. We conduct a thorough analysis of diffusion model parameters, revealing optimal configurations that enhance adversarial robustness while maintaining the integrity of normal performance. Notably, we provide insights into the intricate relationship between diffusion noise and the number of diffusion steps and leverage them to optimize the diffusion process. Our comprehensive experiments across two prominent datasets and against five different adversarial attacks underscore the strong purification potential of diffusion models, marking a pivotal contribution to enhancing the resilience of intrusion detection systems against adversarial threats. For further research, we make our implementation code is publicly available on Github .