## 9 Conclusions and Limitations  
This study conducted a comprehensive systematic literature review on existing ML, DL, and combined approaches proposed to detect attacks in RPL-based 6 LoWPAN networks. The reviewed studies were from five databases published from January 2016 to mid-2021. After several filtering stages, the final count of articles selected for the review process was 49. Then, we analyzed the existing studies from the 49 articles to answer our defined research questions related to bibliographical info, techniques, programs, tools, performance evaluation metrics, datasets, and limitations. The findings reveal that the number of publications within the RPL domain has increased rapidly in recent years. Most researchers have published their work in the IEEE Xplore(r) database in the form of journaled articles. Afterward, we discovered that most studies focused on ML mechanism implementation with promising results, followed by DL and combined approaches. Furthermore, we noted that the VN and HF attacks are the most prevalent in the existing studies. In contrast, some attacks received less attention from researchers, which might threaten the RPL networks.  
On the other hand, we realized that most researchers used Contiki OS (Cooja Simulator) to implement their proposed approaches. As for the metrics used, most researchers computed accuracy metrics in their work, which signifies its importance in the performance evaluation stage. However, we also noticed that most studies ignored some crucial parameters, such as models, approaches, and systems, in evaluating their proposed solutions. As for the datasets, many researchers did not disclose the dataset used in their works, but those that do mostly use synthetic datasets.  
Additionally, some researchers use public datasets created using traditional network traffic, which is unsuitable for RPL-based 6 LoWPAN. In this regard, we gave some insights into the used and available datasets in the RPL security research domain. Additionally, we illustrated the current challenges of reviewed studies and some security issues related to the RPL network.  
As for this study's limitations, first, this study is limited to a few selected databases, although they are considered the most reliable sample of sources related to the research topic of this study. Nevertheless, other indexed databases might also contain studies relevant to this topic. However, we omitted those resources due to subscription requirements, limiting our access to their databases.  
Second, this SLR study is limited to ML, DL, and combined approaches that detect RPL-based attacks, as presented in Figure 3. In addition, this study provides a comprehensive review and critical analysis focusing only on the recent ML, DL, and combined ML- and DL-based approaches for detecting RPL-based attacks, along with existing benchmark datasets used to evaluate their effectiveness.  
Third, there are many SLR studies related to this topic using different sets of research keywords (see Section 4.1). However, to the best of our knowledge, there are no standard keywords related to RPL and 6 LoWPAN networks exist. Moreover, our search is limited to English-language studies with a restricted range of publication years (January 2016 to mid-2021).  
Finally, we suggested some potential research directions that will serve as a solid foundation for future researchers in the RPL domain.  
Development of ideas, design of systematic literature review study, analysis, and article writing, T.A.A.-A. and M.A.; writing--review and editing, M.A., A.H.H.K., and I.H.H.; writing--review and editing, guidance, and supervision, M.A. and B.B.; editing--first draft preparation, I.H.H. and Z.R.A.; funding acquisition, M.A. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a Research University (RU) Grant, Universiti Sains Malaysia (USM) (No: 1001.PNAV.8011107).  
Not applicable.  
The authors declare no conflict of interest.