A Smart Network Intrusion Detection System for Cyber Security of Industrial IoT

The industrial sector has been making use of machines and sensors having the capability to communicate with each other. This forms a network of Industrial devices which is called Industrial Internet of Things (IIoT). IIoT is an emerging trend that can generate a huge amount of data that is vulnerable to cyber-attacks. In IIoT network, data and control instructions flow through Supervisory Control and Data Acquisition (SCADA) system. A Network Intrusion Detection System (NIDS) which can monitor realtime network traffic could be deployed between SCADA and the IIoT devices to detect cyber-attacks. NIDS with Deep Learning (DL) algorithms require large dataset for which CSE-CIC-IDS2018 and UNSW-NB15 dataset is used. The paper compares a Multi-Layer Perceptron(MLP), a Fully Connected Deep Neural Network (FCNN), and Convolutional Neural Network (CNN) on CSE-CIC-IDS2018 and UNSW-NB15 with XGBoost for feature selection.