Malicious intrusions are constant threats to networks, intrusion detection systems (IDS) have been developed to identify and classify these attacks to prevent them from occurring. However, the accuracy and efficiency of these systems are still not satisfactory. Currently, the actual detection accuracy of some detection models is relatively low. Most earlier research approaches relied on regular neural networks, which had low accuracy. The IDS works well while machine learning (ML) and especially deep learning (DL) algorithms are employed to identify and prevent various threats. To solve these problems, an enhancing Bidirectional long short-term memory (Bi-LSTM) model has been proposed in this paper. To train our model, the up-to-date publicly available NSL-KDD dataset is introduced, which is a widely used benchmark dataset in the field of intrusion detection. We have performed 10-fold cross-validation to demonstrate the unbiasedness of the results. Furthermore, we compare the enhancing Bi-LSTM model with existing classifiers. Our proposed model achieves impressive results in terms of accuracy up to 97,93%.