The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
A hybrid deep learning model for efficient intrusion detection in big data environment
Abstract
Abstract The volume of network and Internet traffic is expanding daily, with data being created at the zettabyte to petabyte scale at an exceptionally high rate. These can be characterized as big data, because they are large in volume, variety, velocity, and veracity. Security threats to networks, the Internet, websites, and organizations are growing alongside this growth in usage. Detecting intrusions in such a big data environment is difficult. Various intrusion-detection systems (IDSs) using artificial intelligence or machine learning have been proposed for different types of network attacks, but most of these systems either cannot recognize unknown attacks or cannot respond to such attacks in real time. Deep learning models, recently applied to large-scale big data analysis, have shown remarkable performance in general but have not been examined for detection of intrusions in a big data environment. This paper proposes a hybrid deep learning model to efficiently detect network intrusions based on a convolutional neural network (CNN) and a weight-dropped, long short-term memory (WDLSTM) network. We use the deep CNN to extract meaningful features from IDS big data and WDLSTM to retain long-term dependencies among extracted features to prevent overfitting on recurrent connections. The proposed hybrid method was compared with traditional approaches in terms of performance on a publicly available dataset, demonstrating its satisfactory performance.
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Ullah, I., & Mahmoud, Q. H. (2022) · IEEE Access · doi:10.1109/access.2022.3176317 · Cited by 270
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Ferrag, M. A., Shu, L., Djallel, H., & Choo, K.-K. R. (2021) · Electronics · doi:10.3390/electronics10111257 · Cited by 253
Securing Industrial Internet of Things Against Botnet Attacks Using Hybrid Deep Learning Approach
Hasan, T., Malik, J., Bibi, I., Khan, W. U., Al-Wesabi, F. N., Dev, K., & Huang, G. (2023) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3168533 · Cited by 121
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Metadata
- Authors
- Hassan, M. M., Gumaei, A., Alsanad, A., Alrubaian, M., & Fortino, G.
- Venue
- Information Sciences
- Publisher
- Elsevier BV
- Published
- 2020-03
- Volume
- 513
- Pages
- 386-396
- ISSN
- 0020-0255 (ISSN-L)
- OpenAlex
- W2986055611
- Cited by
- 401
- References
- 30
- FWCI
- 22.58
- Type
- journal article
- Language
- en
- Open access
- closed
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- Hassan, M. M., Gumaei, A., Alsanad, A., Alrubaian, M., & Fortino, G. (2020). A hybrid deep learning model for efficient intrusion detection in big data environment. Information Sciences. https://doi.org/10.1016/j.ins.2019.10.069
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