 A network intrusion detection system, NIDS, helps system administrators to detect network security breaches in their organizations. However, many challenges arise while developing a flexible and efficient NIDS for unforeseen and unpredictable attacks. We propose a deep learning-based approach for developing such an efficient and flexible NIDS. We use self-taught learning, STL, a deep learning-based technique, on NSLKDD, a benchmark dataset for network intrusion. We present the performance of our approach and compare it with a few previous work. Compaired metrics include accuracy, precision, recall, and F-measure values. This article was authored by Amon Javade, Quamarniaz, Waching Sun, and others.