This paper proposes a deep learning-based approach for developing an efficient and flexible Network Intrusion Detection System (NIDS). The authors use Self-Taught Learning (STL), a deep learning technique, on the NSL-KDD dataset, which is an improved version of the KDD Cup 99 dataset, to address the challenges of detecting unknown and unpredictable attacks. The STL approach involves two stages: unsupervised feature learning using a sparse autoencoder and supervised classification using soft-max regression. The paper evaluates the performance of the proposed NIDS using metrics such as accuracy, precision, recall, and F-measure, comparing it with previous methods. The results show that the proposed NIDS achieves high accuracy, particularly in 2-class and 5-class classifications, outperforming other methods like soft-max regression (SMR) and traditional machine learning algorithms. The authors conclude by suggesting future directions, including real-time implementation and on-the-go feature learning on raw network traffic headers.This paper proposes a deep learning-based approach for developing an efficient and flexible Network Intrusion Detection System (NIDS). The authors use Self-Taught Learning (STL), a deep learning technique, on the NSL-KDD dataset, which is an improved version of the KDD Cup 99 dataset, to address the challenges of detecting unknown and unpredictable attacks. The STL approach involves two stages: unsupervised feature learning using a sparse autoencoder and supervised classification using soft-max regression. The paper evaluates the performance of the proposed NIDS using metrics such as accuracy, precision, recall, and F-measure, comparing it with previous methods. The results show that the proposed NIDS achieves high accuracy, particularly in 2-class and 5-class classifications, outperforming other methods like soft-max regression (SMR) and traditional machine learning algorithms. The authors conclude by suggesting future directions, including real-time implementation and on-the-go feature learning on raw network traffic headers.