A deep learning approach for network intrusion detection system

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Abstract

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.

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APA

Niyaz, Q., Sun, W., Javaid, A. Y., & Alam, M. (2015). A deep learning approach for network intrusion detection system. In EAI International Conference on Bio-inspired Information and Communications Technologies (BICT). https://doi.org/10.4108/eai.3-12-2015.2262516

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