 The proposed hybrid approach combines the advantages of rough set theory and intuitionistic fuzzy set theory to detect anomalies in computer network and database systems. It uses natural language expressions to classify data instances, allowing for greater accuracy and identifying anomalous behavior. Empirical studies show that the proposed algorithm achieves high true positive rates of 91.989% and 96.99% for normal behavior and 91.289% and 96.29% for attack behavior, respectively. This article was authored by Fokrel Al-Mazarbiyah and Mohamed Shenafi.