Intrusion Detection in Industrial Internet of Things Based on Recurrent Rule-Based Feature Selection
Mohanarangan Veerappermal Devarajan, Srinivas Aluvala, Vinaye Armoogum, Sivanpillai Sureshkumar, H T Manohara · 2024
The Industrial Internet of Things (IIoT) is experiencing rapid growth, and robust cyber security measures to protect against from the cyber-attacks with the help of Anomaly Detection System (ADS) and signature-based detection system. The sensors collect vast amount data from the environment, presenting functionality challenges for device. To overcome this problem, many Network Intrusion Detection Systems (NIDS) had developed for secure to IIoT systems. But NIDS faces challenges due to complexity of information collection required for threat detection. So, propose study work introduces a Recurrent rule-based Feature Selection (RFS) for IIoT system. NSL-KDD and UNSW-NB15 dataset are used for performing operation and relevant features are selected by using hybrid rule-based algorithm. Then RFS model classifies the data and predict the attacks. the proposed method performance is superior than existing method and better results in terms of accuracy rates of 99.0% and 98.9%, detection rates of 99.0% and 99.9%, and low false positive rates of $1.0 \%$ and $1.1 \%$ respectively.