A Hybrid Deep Learning based Intrusion Detection Framework to Identify Cyber Attacks in Edge-based IIoT

The rise of the Industrial Internet of Things (IIoT) has brought a new era of automated communication to the industrial sector. However, increased connectivity and the use of low-power devices make IIoT networks highly vulnerable to security threats. This paper presents a hybrid intrusion detection system (IDS) for edge-based IIoT devices that uses two deep learning (DL) classifiers, the Sparse Auto Encoder (SAE) and the Bidirectional Long-Short Term Memory (BLSTM), to identify specific attack categories and provide early warning for security analysts. The SAE extracts patterns of the IIoT network through its sparse coding technique, while the BLSTM uses the output of the SAE to identify potential threats and intrusions. The proposed framework can pinpoint specific attack categories and aid security analysts in providing early warning and adopting proactive defensive methods. The edge-based approach improves routing flexibility and promotes interoperability between heterogeneous IIoT devices. The proposed framework achieved 99.61 % and 99.22 % accuracy under CICDDoS2019 and CICID2018 datasets and outperforms traditional and contemporary intrusion detection methods.