With attackers discovering more inventive ways to take advantage of network weaknesses, the pace of attacks has drastically increased in recent years. As a result, network security has never been more important, and many network intrusion detection systems (NIDS) rely on old, out-of-date attack signatures. This necessitates the deployment of reliable and modern Network Intrusion Detection Systems that are educated on the most recent data and employ deep learning techniques to detect malicious activities. However, it has been found that the most recent datasets readily available contain a large quantity of benign data, enabling conventional deep learning systems to train on the imbalance data. A high false detection rate result from this. To overcome the aforementioned issues, we suggest a Synthetic Minority Over-Sampling Technique (SMOTE) integrated convolution neural network and bi-directional long short-term memory SCNN-BIDLSTM solution for creating intrusion detection systems. By employing the SMOTE, which integrates a convolution neural network to extract spatial features and a bi-directional long short-term memory to extract temporal information; difficulties are reduced by increasing the minority samples in our dataset. In order to train and evaluate our model, we used open benchmark datasets as CIC-IDS2017, NSL-KDD, and UNSW-NB15 and compared the results with other state of the art models.