In recent years, machine learning-based Network Intrusion Detection Systems have been widely investigated to detect network attacks. The performance of such systems is strongly affected by their configuration, i.e. the setting of the hyper-parameters, usually based on human expertise. Few efforts have been made towards automatic methods except using a long process of trials. Besides, the resulting configuration is specific to the network where the system is deployed or the type of attacks to detect. To address these issues, we define a method using metalearning which learns from the past experiences. By extracting useful information from the previous optimized tuning tasks, a model is trained in order to quickly infer a new configuration. In comparison with Bayesian optimization, our evaluation based on the CSE CIC IDS2018 and CIC IDS2017 datasets demonstrates that our lightweight technique does not degrade attack detection accuracy in 88% of cases but is on average 9 times faster.