The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
A Deep Learning Approach for Intrusion Detection in Internet of Things using Bi-Directional Long Short-Term Memory Recurrent Neural Network
Abstract
Internet of Things (IoT) is one of the most rapidly evolving technologies nowadays. It has its impact in various industrial sectors including logistics tracking, medical fields, automobiles and smart cities. With its immense potentiality, IoT comes with crucial security concerns that need to be addressed. In this paper, we present a novel deep learning technique for detecting attacks within the IoT network using Bi-directional Long Short-Term Memory Recurrent Neural Network (BLSTM RNN). A multi-layer Deep Learning Neural Network is trained using a novel benchmark data set: UNSWNB15. This paper focuses on the binary classification of normal and attack patterns on the IoT network. The experimental outcomes show the efficiency of our proposed model with regard to precision, recall, f-1 score and FAR. Our proposed BLSTM model achieves over 95% accuracy in attack detection. The experimental outcome shows that BLSTM RNN is highly efficient for building high accuracy intrusion detection model and offers a novel research methodology.
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LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Bidirectional recurrent neural networks
Schuster, M., & Paliwal, K. K. (1997) · IEEE Transactions on Signal Processing · doi:10.1109/78.650093 · Cited by 10,172
A Design Science Research Methodology for Information Systems Research
Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007) · Journal of Management Information Systems · doi:10.2753/mis0742-1222240302 · Cited by 7,832 · cited 1×
“Moustafa and Slay (in 2015) [14] suggested that the NSL-KDD dataset and KDD’99 dataset did not characterize the up-to-date features for intrusion detection, and presented a comprehensive and all-inclusive dataset called the UNSWNB15.”
BackgroundA Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks
Yin, C., Zhu, Y., Fei, J., & He, X. (2017) · IEEE Access · doi:10.1109/access.2017.2762418 · Cited by 2,100
The Internet of Things: New Interoperability, Management and Security Challenges
Elkhodr, M., Shahrestani, S., & Cheung, H. (2016) · arXiv · doi:10.48550/arxiv.1604.04824 · Cited by 182
Self-citation
Cited by
Design and Development of RNN Anomaly Detection Model for IoT Networks
Ullah, I., & Mahmoud, Q. H. (2022) · IEEE Access · doi:10.1109/access.2022.3176317 · Cited by 270
Moustafa, N., Koroniotis, N., Keshk, M., Zomaya, A. Y., & Tari, Z. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3280465 · Cited by 266
Internet of Things attack detection using hybrid Deep Learning Model
Sahu, A. K., Sharma, S., Tanveer, M., & Raja, R. (2021) · Computer Communications · doi:10.1016/j.comcom.2021.05.024 · Cited by 258
Kumar, R., Kumar, P., Tripathi, R., Gupta, G. P., Garg, S., & Hassan, M. M. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3122021 · Cited by 50
Metadata
- Authors
- Roy, B., & Cheung, H.
- Venue
- 2018 28th International Telecommunication Networks and Applications Conference (ITNAC)
- Publisher
- IEEE
- Published
- 2018-11
- Pages
- 1-6
- OpenAlex
- W2909665293
- Cited by
- 196
- References
- 5
- FWCI
- 11.28
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Roy, B., & Cheung, H. (2018). A Deep Learning Approach for Intrusion Detection in Internet of Things using Bi-Directional Long Short-Term Memory Recurrent Neural Network. 2018 28th International Telecommunication Networks and Applications Conference (ITNAC). https://doi.org/10.1109/atnac.2018.8615294
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