The growing complexity of cyberattacks has posed significant challenges to network intrusion detection systems (IDS). Despite being equipped with sophisticated machine learning capabilities, intelligent IDSs have vulnerabilities that can be exploited by adversarial algorithms which are widely known to inject subtle perturbations into data passing through IDSs. This paper evaluates the impact of two adversarial black-box attacks - Gaussian Perturbation and Genetic Algorithm - on the performance of machine learning(ML)-based IDS model, by thoroughly investigating how these adversarial algorithms affect the integrity of data and model performance. Our research contributes to a deeper understanding of how adversarial attacks generate deceptive data and underscores the importance of developing innovative strategies to defend the defenders, i.e., IDS systems.