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Botnet Detection in the Internet of Things using Deep Learning Approaches
Open accessAbstract
The recent growth of the Internet of Things (IoT) has resulted in a rise in IoT based DDoS attacks. This paper presents a solution to the detection of botnet activity within consumer IoT devices and networks. A novel application of Deep Learning is used to develop a detection model based on a Bidirectional Long Short Term Memory based Recurrent Neural Network (BLSTM-RNN). Word Embedding is used for text recognition and conversion of attack packets into tokenised integer format. The developed BLSTM-RNN detection model is compared to a LSTM-RNN for detecting four attack vectors used by the mirai botnet, and evaluated for accuracy and loss. The paper demonstrates that although the bidirectional approach adds overhead to each epoch and increases processing time, it proves to be a better progressive model over time. A labelled dataset was generated as part of this research, and is available upon request.
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Cited by
A Survey of Deep Learning Methods for Cyber Security
Berman, D. S., Buczak, A. L., Chavis, J. S., & Corbett, C. L. (2019) · Information · doi:10.3390/info10040122 · Cited by 569
Deep Learning Models for Cyber Security in IoT Networks
Roopak, M., Yun Tian, G., & Chambers, J. (2019) · 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc.2019.8666588 · Cited by 361
Detecting Internet of Things attacks using distributed deep learning
De La Torre Parra, G., Rad, P., Choo, K.-K. R., & Beebe, N. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102662 · Cited by 328
An Unsupervised Deep Learning Model for Early Network Traffic Anomaly Detection
Hwang, R.-H., Peng, M.-C., Huang, C.-W., Lin, P.-C., & Nguyen, V.-L. (2020) · IEEE Access · doi:10.1109/access.2020.2973023 · Cited by 282
Internet of Things: Evolution, Concerns and Security Challenges
Malhotra, P., Singh, Y., Anand, P., Bangotra, D. K., Singh, P. K., & Hong, W.-C. (2021) · Sensors · doi:10.3390/s21051809 · Cited by 260
Metadata
- Authors
- McDermott, C. D., Majdani, F., & Petrovski, A. V.
- Venue
- 2018 International Joint Conference on Neural Networks (IJCNN)
- Publisher
- IEEE
- Published
- 2018-07
- Pages
- 1-8
- OpenAlex
- W2801118360
- Cited by
- 350
- References
- 0
- FWCI
- 25.06
- Type
- conference paper
- Language
- en
- Open access
- green
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- McDermott, C. D., Majdani, F., & Petrovski, A. V. (2018). Botnet Detection in the Internet of Things using Deep Learning Approaches. 2018 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/ijcnn.2018.8489489
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