An Intrusion Detection System Using a Deep Neural Network With Gated Recurrent Units
| Autor: | Jizhong Shen, Congyuan Xu, Xin Du, Fan Zhang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
General Computer Science
Computer science Feature extraction 02 engineering and technology Intrusion detection system gated recurrent unit 0202 electrical engineering electronic engineering information engineering Intrusion detection General Materials Science Network model Artificial neural network business.industry Deep learning General Engineering deep learning 020206 networking & telecommunications Pattern recognition Support vector machine Recurrent neural network Multilayer perceptron Softmax function recurrent neural network 020201 artificial intelligence & image processing lcsh:Electrical engineering. Electronics. Nuclear engineering Artificial intelligence business lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 6, Pp 48697-48707 (2018) |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2018.2867564 |
| Popis: | To improve the performance of network intrusion detection systems (IDS), we applied deep learning theory to intrusion detection and developed a deep network model with automatic feature extraction. In this paper, we consider the characteristics of the time-related intrusion and propose a novel IDS that consists of a recurrent neural network with gated recurrent units (GRU), multilayer perceptron (MLP), and softmax module. Experiments on the well-known KDD 99 and NSL-KDD data sets show that the system has leading performance. The overall detection rate was 99.42% using KDD 99 and 99.31% using NSL-KDD with false positive rates as low as 0.05% and 0.84%, respectively. In particular, for detecting the denial of service attacks, the system achieved detection rates of 99.98% and 99.55%, respectively. Comparative experiments showed that the GRU is more suitable as a memory unit for IDS than LSTM, and proved that it is an effective simplification and improvement of LSTM. Moreover, the bidirectional GRU can reach the best performance compared with the recently published methods. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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