This paper proposes a deep learning approach for developing an efficient and flexible Network Intrusion Detection System (NIDS). The approach uses Self-Taught Learning (STL), a deep learning technique based on sparse autoencoder and soft-max regression, to learn feature representations from unlabeled network traffic data and apply them to labeled data for classification. The NSL-KDD dataset, an improved version of the KDD Cup 99 dataset, is used for evaluation. The dataset contains 41 features and is used to classify traffic into normal or attack categories. The proposed approach addresses two main challenges in developing NIDS: selecting appropriate features for anomaly detection and the lack of labeled data from real networks. STL first learns a good feature representation from unlabeled data, then applies this representation to labeled data for classification. The performance of the approach is evaluated using metrics such as accuracy, precision, recall, and F-measure. The results show that the proposed NIDS outperforms previous methods in terms of accuracy and F-measure for both 2-class and 5-class classification. The approach is also tested on test data, where it achieves high accuracy and better recall and F-measure values compared to other methods. The study concludes that the proposed deep learning approach is effective for NIDS and suggests future work in real-time NIDS implementation and on-the-fly feature learning.This paper proposes a deep learning approach for developing an efficient and flexible Network Intrusion Detection System (NIDS). The approach uses Self-Taught Learning (STL), a deep learning technique based on sparse autoencoder and soft-max regression, to learn feature representations from unlabeled network traffic data and apply them to labeled data for classification. The NSL-KDD dataset, an improved version of the KDD Cup 99 dataset, is used for evaluation. The dataset contains 41 features and is used to classify traffic into normal or attack categories. The proposed approach addresses two main challenges in developing NIDS: selecting appropriate features for anomaly detection and the lack of labeled data from real networks. STL first learns a good feature representation from unlabeled data, then applies this representation to labeled data for classification. The performance of the approach is evaluated using metrics such as accuracy, precision, recall, and F-measure. The results show that the proposed NIDS outperforms previous methods in terms of accuracy and F-measure for both 2-class and 5-class classification. The approach is also tested on test data, where it achieves high accuracy and better recall and F-measure values compared to other methods. The study concludes that the proposed deep learning approach is effective for NIDS and suggests future work in real-time NIDS implementation and on-the-fly feature learning.