A Deep Learning Approach for Network Intrusion Detection System

Javaid, Ahmad and Niyaz, Quamar and Sun, Weiqing and Alam, Mansoor (2016) A Deep Learning Approach for Network Intrusion Detection System. EAI Endorsed Transactions on Security and Safety, 3 (9). e2. ISSN 2032-9393

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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.

Item Type: Article
Uncontrolled Keywords: network security, nids, deep learning, sparse autoencoder, nsl-kdd
Subjects: H Social Sciences > H Social Sciences (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor IV
Date Deposited: 26 Mar 2021 13:51
Last Modified: 26 Mar 2021 13:51
URI: https://eprints.eudl.eu/id/eprint/2057

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