Undoubtedly, the advancements in Machine Learning (ML) and especially ensemble learning models enable researchers to develop numerous fields in ways we had never imagined before. Intrusion Detection System (IDS) is one of these fields that benefits from the aforementioned techniques to identify and classify the security threats with more accuracy. In fact, IDS technology has become an essential component for any defense strategy, since it provides a healthy environment for businesses. Besides, it protects future network infrastructures from intruders and suspicious network activities. In this context, this paper presents a systematic literature review on ensemble learning-based IDS and the datasets used to evaluate the proposed methods. Furthermore, we illustrate the distribution of the reviewed papers by year of publication, datasets utilized and ensemble learning methods. Finally, we discuss the finding of this review and highlight some limitations of the aforementioned models. The overall purpose of this literature review is to provide a guideline on how to choose ensemble learning models to solve IDS issues based on the most efficient and recent trends

Regarding the ever-growing danger of malicious incidents and attacks on the Internet, researchers on Intrusion Detection System (IDS) are moving towards more co