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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 NSL-KDD - a benchmark dataset for network

intrusion. We present the performance of our approach and compare it with a few previous work. Compared metrics include accuracy, precision, recall, and f-measure values.