A Deep Learning Approach for Network Intrusion Detection System
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 de...
Main Authors: | , , , |
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Format: | Article |
Language: | English |
Published: |
European Alliance for Innovation (EAI)
2016-12-01
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Series: | EAI Endorsed Transactions on Security and Safety |
Subjects: | |
Online Access: | http://eudl.eu/doi/10.4108/eai.3-12-2015.2262516 |
Summary: | 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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ISSN: | 2032-9393 |