The network infrastructure of any organization is always under constant threat to a variety of attacks; namely, break-ins, security breach or system misuse. The Network Intrusion Detection System (NIDS) employed in a network detects such penetration attacks and intrusions within a network. Known classes of attacks can be detected easily by performing pattern matching while the unknown attacks are harder to detect. An attempt has been made to design a system using a deep learning approach for intrusion detection that not only learns but also adjusts itself to the patterns not defined earlier. Sparse auto-encoder has been used for unsupervised feature learning. Logistic classifier is then utilized for classification on NSL-KDD dataset. The performance of the system has been measured with respect to accuracy, precision and recall and the results have been found to be very promising for future use and modifications.