In recent years, the Industrial Internet of things (IIoT) is a fastest advancing innovative technology with a potential to digitize and interconnect many industries for huge business opportunities and development of global GDP. IIoT is used in diverse range of industries such as manufacturing, logistics, transportation, oil and gas, mining and metals, energy utilities and aviation. Although IIoT provides promising opportunities for the development of different industrial applications, they are prone to cyberattacks and demands for higher security requirements. The enormous number of sensors present in the IIoT network generates a large amount of data and has attracted the attention of cybercriminals across globe. The intrusion detection system (IDS) that monitors the network traffic and detects the behaviour of the network is considered as one of the key security solution for securing IIoT application from attacks. Recently, the application of machine and deep learning techniques have proved to mitigate multiple security threats and enhance the performance of intrusion detection. In this paper, we present a survey of deep learning-based IDS technique for IIoT. The main objective of this research is to provide the various deep learning-based IDS detection methods, datasets and comwparative analysis. Finally, this research aims to identify the limitations and challenges of existing studies, solutions and future directions.

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