A Deep Learning Approach for Network Intrusion Detection System

Authors

DOI:

https://doi.org/10.4108/eai.3-12-2015.2262516

Keywords:

network security, nids, deep learning, sparse autoencoder, nsl-kdd

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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Published

24-05-2016

How to Cite

Javaid, A. ., Niyaz, Q. ., Sun, W. ., & Alam, M. . (2016). A Deep Learning Approach for Network Intrusion Detection System. EAI Endorsed Transactions on Security and Safety, 3(9), e2. https://doi.org/10.4108/eai.3-12-2015.2262516

Issue

Vol. 3 No. 9 (2016): EAI Endorsed Transactions on Security and Safety

Section

Research article

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Copyright (c) 2022 EAI Endorsed Transactions on Security and Safety

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