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The recent innovations in the network communication domain have entirely revolutionized the conventional industrial sector by introducing a new era of automatic communication. The Industrial Internet of Things (IIoT) is acknowledged as an exclusive space where Internet of Things (IoT) seems to divulge expressive manifestations. However, the expanded connectivity, more openness, and the widespread use of low-power communication devices of IIoT makes them vulnerable to malicious attacks, and criminal activities. Moreover, the heterogeneous and prevalent nature of the IIoT devices makes it very difficult to come up with a centralized threat detection mechanism, thus, its security remains a major concern. Motivated by this, we propose a fog-empowered augmented intelligence (IA)-based defensive mechanism to ensure secure communication in IoT-enabled smart industries. The designed framework incorporates the aggregated potentials of two highly acclaimed deep learning (DL) classifiers: 1) gated recurrent unit (GRU) and 2) bidirectional long short-term memory (BiLSTM), where the DL-based scheme detects anomalies in such industrial networks and the FOG-based scenario promotes the routing flexibility and interoperability of the heterogeneous devices of the IoT-enabled smart industrial network. The validity of the proposed mechanism is analytically investigated by conducting a comparison with the benchmark threat detection techniques. The proposed solution is also generically examined parallel to some state-of-the-art approaches. The Cu-GRU-BiLSTM framework achieved up to 99.91% accuracy with a low-false alarm rate and outperforms the baseline and recent threat detection approaches. The simulation and comparison results validate the effectiveness of the proposed mechanism and advocate it as a phenomenal choice to ensure efficacious and secure communication in IoT-based smart industries.
Javeed et al. (Sat,) studied this question.