| Title | DRaNN: A Deep Random Neural Network Model for Intrusion Detection in Industrial IoT |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Latif, Shahid, Idrees, Zeba, Zou, Zhuo, Ahmad, Jawad |
| Conference Name | 2020 International Conference on UK-China Emerging Technologies (UCET) |
| Date Published | aug |
| Keywords | Cyber-physical systems, cybersecurity, Deep Learning, feature extraction, Internet of Things, Intrusion detection, machine learning, Metrics, Neural Network Security, Neural networks, Neurons, policy-based governance, pubcrawl, random neural network, Resiliency, Training |
| Abstract | Industrial Internet of Things (IIoT) has arisen as an emerging trend in the industrial sector. Millions of sensors present in IIoT networks generate a massive amount of data that can open the doors for several cyber-attacks. An intrusion detection system (IDS) monitors real-time internet traffic and identify the behavior and type of network attacks. In this paper, we presented a deep random neural (DRaNN) based scheme for intrusion detection in IIoT. The proposed scheme is evaluated by using a new generation IIoT security dataset UNSW-NB15. Experimental results prove that the proposed model successfully classified nine different types of attacks with a low false-positive rate and great accuracy of 99.54%. To validate the feasibility of the proposed scheme, experimental results are also compared with state-of-the-art deep learning-based intrusion detection schemes. The proposed model achieved a higher attack detection rate of 99.41%. |
| DOI | 10.1109/UCET51115.2020.9205361 |
| Citation Key | latif_drann_2020 |
DRaNN: A Deep Random Neural Network Model for Intrusion Detection in Industrial IoT