###### Abstract  
The Internet of Things (IoT) is a prominent and advanced network communication technology that has familiarized the world with smart industries. The conveniently acquirable nature of IoT makes it susceptible to a diversified range of potential security threats. The literature has brought forth a plethora of solutions for ensuring secure communications in IoT-based smart industries. However, resource-constrained sectors still demand significant attention. We have proposed a fog-assisted deep learning (DL)-empowered intrusion detection system (IDS) for resource-constrained smart industries. The proposed Cuda-deep neural network gated recurrent unit (Cu-DNNGRU) framework was trained on the N-BaloT dataset and was evaluated on judicious performance metrics, including accuracy, precision, recall, and F1-score. Additionally, the Cu-DNNGRU was empirically investigated alongside state-of-the-art classifiers, including Cu-LSTMDNN, Cu-BLSTM, and Cu-GRU. An extensive performance comparison was also undertaken among the proposed IDS and some outstanding solutions from the literature. The simulation results showed ample strength with respect to the validation of the proposed framework. The proposed Cu-DNNGRU achieved 99.39% accuracy, 99.09% precision, 98.89% recall, and an F1-score of 99.21%. In the performance comparison, the values were substantially higher than those of the benchmarked schemes, as well as competitive security solutions from the literature. Industrial Internet of Things (IIoT); fog computing; deep learning (DL); RPL; intrusion detection system (IDS) +
Footnote †: journal: Sensors  
RPL protocol to expedite the routing mechanisms of resource-constrained networks [11]. This protocol operates at the physical standard of IEEE802.15.4 and is considered an optimal choice for ensuring reliable communication in low-power and lossy networks [12; 13]. The application circle of RPL is expanding, and its applications can be observed in every IoT-based communication environment. The industrial sector is one of the highly privileged application areas of RPL, as the limited availability of resources and frequent communication breakages are crucial concerns in the industrial environment. RPL is a potential choice for coping with such emerging challenges in the industrial sector [14; 15; 16]. The expanding range of RPL applications in industrial sectors clearly shows its efficiency. However, these circumstances make RPL networks vulnerable to various potential risks [17]. Such challenging circumstances demand multiple security solutions in order to ensure durable and satisfactory communication in industrial networks [18]. Artificial intelligence (AI) [19], fog computing [20], software-defined networking (SDN) [21], machine learning (ML) [22], and deep learning (DL) [23; 24; 25] have been used to address this. In the present era, combinations of deep-learning- and fog-computing-based schemes are considered fascinating solutions to overcome such challenges [26]. Fog computing provides a decentralized security approach by dividing the functional roles among various fog nodes. Hence, it prohibits the resource utilization of a particular node, making it an unusual approach to efficient resource management [27]. Secondly, it provides a two-layer-based surveillance architecture that is capable of conducting an investigation of malicious entities in a network [28]. Deep-learning-based approaches deliver an evolutionary mechanism for analyzing the traffic streams cascading over a network [29]. A DL-based system is first trained and tested on a dataset that contains existing impressions of an immense range of suspicious activities [30]. There is a large catalog of training datasets, e.g., NSL-KDD [31], UNSW-NB15 [32], BOT-IoT [33], ADFA-LD [34], CICIDS2017 [35], and N-BaIoT [36], which can be employed to train the concerned systems. Further, such intrusion detection systems are used in virtual environments to investigate anonymous anomalies within networks [37].