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A Smart Network Intrusion Detection System for Cyber Security of Industrial IoT
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  • Hardik Gunjal ,
  • Preetkumar Patel ,
  • Dr Dariush Ebrahimi ,
  • Dr Fadi Alzhouri
Hardik Gunjal
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Preetkumar Patel
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Dr Dariush Ebrahimi
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Dr Fadi Alzhouri
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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.