A Deep Learning Approach for Network Intrusion Detection System
نویسندگان
چکیده
A Network Intrusion Detection System (NIDS) helps system administrators to detect network security breaches in their organization. However, many challenges arise while developing a flexible and effective NIDS for unforeseen and unpredictable attacks. In this work, we propose a deep learning based approach to implement such an effective 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 the accuracy, precision, recall, and f-measure values.
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