With the recent wave of data analytics accessing every domain, there is a growing interest in handling an imbalanced classification problem. In many datasets, the positive class size is extra smaller compared with the major class (negative class), as in the case of disease detection, cyber-attacks, and many data mining applications. Among the different algorithms that addressed this problem, Random Forest (RF) attracted many researchers because of its general robustness. But, RSs and other cost-sensitive algorithms are suffering from low sensitivity and low precision according to positive class when dealing with imbalanced dataset problem. In this paper, we propose and develop an Entropy-based Fuzzy Random Forest (EFRF) algorithm to deal with imbalanced classification problem. Fuzzy membership is applied to the training instances such that different instances offer different contributions to the classifiers. Samples that have a higher class certainty are assigned to larger fuzzy memberships. EFRF uses the entropy to pay more attention to the samples with higher class certainty to result in more robust decision making to avoid losing information like other undersampling algorithms. The proposed algorithm showed promising results compared to other imbalanced classification techniques including Entropy-based Fuzzy Support Vector Machine (EFSVM) technique. It featured both high precision and high recall which makes it an excellent choice for security-wise application.