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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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.
| 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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