Research Article
A Deep Learning Approach for Network Intrusion Detection System
@INPROCEEDINGS{10.4108/eai.3-12-2015.2262516, author={Ahmad Javaid and Quamar Niyaz and Weiqing Sun and Mansoor Alam}, title={A Deep Learning Approach for Network Intrusion Detection System}, proceedings={9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS)}, publisher={ACM}, proceedings_a={BICT}, year={2016}, month={5}, keywords={network security nids deep learning sparse autoencoder nsl-kdd}, doi={10.4108/eai.3-12-2015.2262516} }- Ahmad Javaid
Quamar Niyaz
Weiqing Sun
Mansoor Alam
Year: 2016
A Deep Learning Approach for Network Intrusion Detection System
BICT
EAI
DOI: 10.4108/eai.3-12-2015.2262516
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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