Intrusion detection systems are essential in providing security to network structures by detecting and reducing the possibility of cyber attacks. This work investigates the usefulness of employing a Deep neural network in combination with the oversampling technique to improve intrusion detection on the CIC-IDS2017 dataset. This dataset is a realistic benchmark dataset that consists of modern network traffic and a variety of attack types. This method uses the synthetic oversampling method to rectify the dataset’s intrinsic class imbalance, which is a significant problem in intrusion detection due to insufficient minority attack classes. Experimental findings show that combining these methods enhances detecting rates of minority attack classes while retaining excellent overall performance. This study highlights the potential for combining sophisticated resampling methods with deep learning algorithms to provide more effective intrusion detection systems to protect against a broad spectrum of cyber threats in current network configurations. The model provided better performance outcomes regarding accuracy, recall, precision, and F1-score compared to other approaches.