## 2 Related Work  
RPL-based resource-constrained smart industries are attaining significant attention where notable efforts are being made towards its security. Here, we addressed some meaningful research studies surrounding this domain. Authors [38] have proposed an IDS by including the appropriate elements of Light GBM to enhance the proposed system's threat detection capabilities. The system is aligned with a customized dataset published by Oakridge Lab, which comes with a comprehensive variety of threat detection features. The same model is designed in [39], where a Convolutional Neural Network (CNN) based threat detection scheme is developed. The model is trained on two commonly known datasets, UNSW-NB15 and CICIDS2017. Simulations are carried out to evaluate the model's validity, and researchers aim to assess this on the testbed. Another attempt is made in [40], where researchers have focused on the combined strength of some well-known classifiers such as the Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU). They have proposed a hybrid model that is trained on the N-BaIoT dataset and is capable of interrogating malicious events in resource-constrained environments. Likewise, a hybrid intrusion detection framework is proposed by using LSTM classifier [41]. UNSW-NB15 and NSLKDD datasets are acquired to train the system, and the system's efficiency is evaluated in a pervasive simulation environment. Researchers have obtained the generic features of the CNN classifier to design an anomaly detection mechanism in the healthcare environment. The framework is trained on the CICIDS2017 dataset and analyzed using rational performance metrics. The system has potentially identified a range of emerging cyber threats in the smart healthcare environment [42]. Another model is designed by using a Deep Neural Network (DNN) based classifier and is potentially trained on UNSW-NB15 and NSLKDD datasets. The obtained result validates the importance of the proposed model [43]. Authors have used Text-CNN classifiers and the KDD99 dataset to propose a threat detection model for smart industries [44]. Single-Hidden Layer Feed-forward Neural Network (SLFN) is one of the best classifiers for responsive intrusion detection in industrial environments. It, with the LSTM classifier, brings additional strength to the system. Researchers have adopted these two classifiers to design a multifunctional threat classification mechanism. Furthermore, the IoT-ID20 dataset is used for training purposes [45]. Another effective threat classification scheme is presented [46], where authors mainly target the less frequently occurring suspicious events in industrial networks. They have observed the real-life scenario for a sustainable period and have organized a customized dataset. The N-BaIoT data set is also integrated into sequence with their customized dataset. Their model is facilitated with Principal Component Analysis (PCA) and deep learning classifiers that offer additional support to instantly identify these attacking circumstances. Researchers in [47] provide an alternative deep learning-based detection mechanism. Multiclass classifiers accompany the BOT-IoT dataset for a highly accurate investigation of suspicious entities. The system has achieved remarkable accuracy in distinguishing between normal and abnormal traffic. For RLL-based smart communication industries, a deep learning-inspired malicious packet filtering mechanism is provided [48]. Researchers have used an embedded DNN classifier that controls the entire processing infrastructure. The proposed approach is capable of handling Denial of Services DoS and port scan attacks. Researchers have designed a multidimensional system consisting mainly of a forest PS classifier to investigate crucial security threats in resource-efficient smart industrial environments. They have used a CICIDS2017 dataset containing details of potentially harmful events. The designed model obtained impressive accuracy with high precision and an F1 score [49].