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Industrial Internet of Things (IIoT) networks involves heterogeneous technological and manufacturing services and devices. The communication and data exchange characteristics of IIoT systems and their associated networks make them susceptible to cyberattacks. Delivering IIoT systems with vigorous safety and speedy attack discovery is therefore vital. Intrusion detection systems (IDS) have been widely employed to detect cyber-attack events from Internet Industrial Control Systems (IICS) and their networks. Most recently, various attacks, such as flame, duqu and seismic attacks, against IICS setups have prompted excessive damage to nuclear and critical infrastructures in numerous countries. The existing intrusion detection methods generally lack sufficient generalization, misclassification errors and have high false alarm rates. To this end, this paper presents a deep-autoencoder based IDS to distinguish …