Visible to the public DRaNN: A Deep Random Neural Network Model for Intrusion Detection in Industrial IoT

TitleDRaNN: A Deep Random Neural Network Model for Intrusion Detection in Industrial IoT
Publication TypeConference Paper
Year of Publication2020
AuthorsLatif, Shahid, Idrees, Zeba, Zou, Zhuo, Ahmad, Jawad
Conference Name2020 International Conference on UK-China Emerging Technologies (UCET)
Date Publishedaug
KeywordsCyber-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
AbstractIndustrial 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%.
Citation Keylatif_drann_2020