A Simple Recurrent Unit Model Based Intrusion Detection System With DCGAN Article Swipe
Jin Yang
,
Tao Li
,
Gang Liang
,
Wenbo He
,
Yue Zhao
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/access.2019.2922692
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/access.2019.2922692
Due to the complex and time-varying network environments, traditional methods are difficult to extract accurate features of intrusion behavior from the high-dimensional data samples and process the high-volume of these data efficiently. Even worse, the network intrusion samples are submerged into a large number of normal data packets, which leads to insufficient samples for model training; therefore it is accompanied by high false detection rates. To address the challenge of unbalanced positive and negative learning samples, we propose using deep convolutional generative adversarial networks (DCGAN), which allows features to be extracted directly from the rawdata, and then generates new training-sets by learning from the rawdata. Given the fact that the attack samples are usually intra-dependent time sequence data, we apply long short-term memory (LSTM) to automatically learn the features of network intrusion behaviors. However, it is hard to parallelize the learning/training of the LSTM network, since the LSTM algorithm depends on the result of the previous moment. To remove such dependency and enable intrusion detection in real time, we propose a simple recurrent unit based (SRU)-based model. The proposed model was verified by extensive experiments on the benchmark datasets KDD'99 and NSL-KDD, which effectively identifies normal and abnormal network activities. It achieves 99.73% accuracy on the KDD'99 dataset and 99.62% on the NSL-KDD dataset.
Related Topics
Concepts
Computer science
Intrusion detection system
Benchmark (surveying)
Network packet
Artificial intelligence
Deep learning
Process (computing)
Dependency (UML)
Data mining
Moment (physics)
Machine learning
Pattern recognition (psychology)
Operating system
Geography
Computer network
Physics
Classical mechanics
Geodesy
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2019.2922692
- https://ieeexplore.ieee.org/ielx7/6287639/8600701/08736331.pdf
- OA Status
- gold
- Cited By
- 68
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2951944995
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2951944995Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2019.2922692Digital Object Identifier
- Title
-
A Simple Recurrent Unit Model Based Intrusion Detection System With DCGANWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-01-01Full publication date if available
- Authors
-
Jin Yang, Tao Li, Gang Liang, Wenbo He, Yue ZhaoList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2019.2922692Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/8600701/08736331.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/6287639/8600701/08736331.pdfDirect OA link when available
- Concepts
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Computer science, Intrusion detection system, Benchmark (surveying), Network packet, Artificial intelligence, Deep learning, Process (computing), Dependency (UML), Data mining, Moment (physics), Machine learning, Pattern recognition (psychology), Operating system, Geography, Computer network, Physics, Classical mechanics, GeodesyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
68Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 7, 2024: 12, 2023: 15, 2022: 12, 2021: 12Per-year citation counts (last 5 years)
- References (count)
-
40Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| counts_by_year[4].year | 2021 |
| counts_by_year[4].cited_by_count | 12 |
| counts_by_year[5].year | 2020 |
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