### Machine Learning (ML) and Deep Learning (DL) Technique for RPL Security  
The existing traditional security mechanisms, such as those based on cryptography, trust, threshold, and Intrusion Detection systems (IDS), cannot detect or prevent RPL-LLN attacks effectively due to differences in the topology, complexity, and dissimilarity in the traffic patterns [18; 20].  
In addition, these mechanisms fail to detect sophisticated and devastating huge attack surfaces and require heavy resources, which drastically deplete the resources of constrained devices and impact the normal operation of network nodes [25]. Furthermore, detecting a complex and a combination of multiple attacks in the network requires an intelligent detection mechanism. Hence, there is a need to design a detection mechanism capable of handling the issues described above. The security mechanisms should be eligible to identify known and unknown (zero-day) attacks with a thorough examination of their actions. Additionally, the vast amounts of data traffic exchanged between the sensors  
Figure 1: Taxonomy of RPL attacks.  
(devices) of the LNN-based IoT network constitutes another challenge that should be considered throughout the design of the detection mechanism.  
The term \"big data\" refers to a high volume of data. Such data necessitate advanced techniques to extract valuable information to analyze legal and harmful behavioral packet patterns. \"Big Data\" is a buzzword that includes methods to extract extremely vital information from the massive amounts of data traffic exchanged in the IoT networks. In other words, not all the data traffic is necessary for further analysis and learning. The advancements in big data technology make it more practical to extract different legitimate and malicious behavioral packet patterns from the immense data traffic [26].  
Unfortunately, conventional intrusion detection methods cannot effectively process large amounts of data, resulting in a lack of useful information. Thus, more intelligent and adaptable mechanisms are needed to expand the detection capabilities of the existing security defenses systems for the next generation of IoT networks. ML learning and DL-based intrusion detection techniques have attracted much interest in enhancing security in IoT networks [27]. With its ability to learn from datasets, ML is particularly suited to complexities that are too complex to be fully explained or performed precisely. Furthermore, ML requires a small amount of data for training and testing but has lower accuracy. On the contrary, DL, a subset of ML, requires a vast quantity of data to train the system and takes a long time, but it usually gives higher accuracy [28; 29]. In that sense, ML and DL are the most successful computational techniques for providing embedded intelligence in the IoT context due to their ability to deal with a tremendous amount of data, maximize feature engineering, learn from latent abnormal patterns, and reduce the time for detecting known and unknown attacks [30; 31; 32; 33]. Thus, the ML and DL approaches improve the IoT security and RPL network in particular and overcome the weaknesses of other conventional solutions.  
Due to the limitations of IoT devices in terms of computing and power resources, designing ML and DL algorithms for the IoT network is a challenging endeavor [34]. In this context, ML and DL techniques are applicable at RPL nodes, fog/edge nodes, and (or) cloud nodes to extract and analyze large-scale data to detect malicious behaviors. As a result, Artificial Intelligence (AI)-assisted security analysis approaches can transform end-to-end IoT security into an intelligence-based monitoring system [35]. Recently, ML- and DL-based security solutions for identifying attacks and countering threats intelligently in the RPL network have become a promising research area and have attracted attention from many researchers to add more to this field.