## 7 Result and Discussion
### RQ2: What Are the Existing ML-Based Approaches to Detect Attacks in RPL-Based 6 LoWPAN?
The results show that their method improves the throughput of the proposed protocol compared to SecTrust-RPL [61]. In addition, when the number of nodes increases, the proposed method reduces the number of expected transfers and enhances the network's throughput, which is also higher than the SecTrust-RPL protocol. An extension of [53] is presented in this work. The authors [7] proposed an efficient anomaly detection approach for identifying attacks in smart hospitals' IoT systems. The proposed approach aims to detect two categories of attacks: e-health-related data attacks and IoT network attacks. The former includes events and health attacks, such as manipulations in temperature, whereas the latter involves unusual variations in heartbeat rates. In contrast, the targeted IoT attacks in this work include Flooding, VN, and Rank attacks. Moreover, an SVM classifier was used for detecting the attacks. The authors used SVM with two kinds of datasets, e-health data and network infrastructure data. Furthermore, in this work, the proposed architecture was validated using a prototype and tested with various scenarios regarding e-health, environment, and network intrusion. In addition, the Cooja network simulator was used to measure the scalability of the proposed approach. As for the experimental result, the SVM model obtained a detection AC of 93.4%, 60.8%, and 91.6% for Rank, Flooding, and VN attacks, respectively. Moreover, for detecting event attacks, the authors reported detection AC of 85.7% and 82% of increasing temperature levels and heart attacks, respectively. Table 2 presents a summary of the ML based approaches with their important parameters, advantages, and limitations. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Ref/Year** & **A1** & **A2** & **A3** & **A4** & **A5** & **A6** & **A7** & **Advantegid and Limitations** \\ \hline \multirow{4}{*}{[51], (2020)} & \multirow{4}{*}{GP} & DT, SVM, & \multirow{4}{*}{Suggian} & \multirow{4}{*}{5} & Synthetic & \multirow{4}{*}{Ball} & \multirow{4}{*}{Suggian} & \multirow{4}{*}{Suggian} & \multirow{4}{*}{Suggian} & \multirow{4}{*}{Suggian} & \multirow{4}{*}{Suggian} & \multirow{4}{*}{[PERSON]} & \multirow{4}{*}{[PERSON]} & \multirow{4}{*}{[PERSON]} & \multirow{4}{*}{[PERSON]} & \multirow{4}{*}{[PERSON]} \\  & & Classifiers & & & & & & & & & & & & \\ \cline{5-10}  & & & & & & & & & & & & & \\ \cline{5-10}  & & & & & & & & & & & & & \\ \hline \multirow{4}{*}{[51], (2021)} & \multirow{4}{*}{NA} & \multirow{4}{*}{[PERSON]} & \multirow{4}{*}{8} & \multirow{4}{*}{11} & \multirow{4}{*}{[PERSON]} & \multirow{4}{*}{VN, Rank, & \multirow{4}{*}{[PERSON]} & \multirow{4}{*}{[PERSON]} & \multirow{4}{*}{[PERSON]} & \multirow{4}{*}{[PERSON]} & \multirow{4}{*}{[PERSON]} & \multirow{4}{*}{[PERSON]} & \multirow{4}{*}{[PERSON]} & \multirow{4}{*}{[PERSON]} & \multirow{4}{*}{[PERSON]} \\  & & & & & & & & & & & & \\ \cline{5-10}  & & & & & & & & & & & & & \\ \cline{5-10}  & & & & & & & & & & & & & \\ \cline{5-10}  & & & & & & & & & & & & & \\ \hline \multirow{4}{*}{[51], (2021)} & Information & \multirow{4}{*}{ANN} & \multirow{4}{*}{8} & \multirow{4}{*}{11} & \multirow{4}{*}{[PERSON]} & \multirow  
**Summary:** Table 2 shows that some ML-based approaches select the optimum subset of features to improve the detection accuracy, classify the network traffic into normal and abnormal behavior, or use ML algorithms for both purposes. However, several works did not provide details about feature selection technique used in their approach [3, 8, 10, 21, 45, 50, 51, 53, 54, 56, 60]. In addition, other studies, such as [7, 8, 21, 45, 50, 51, 53, 59, 60, 62], did not mention the number of features used or selected in their approach. Moreover, two studies [57, 60] did not disclose the type of program used in the implementation step, and did not provide information about the dataset and the feature selection technique used. RQ3: What Are the Prevailing DL Approaches Contributed by Existing Studies to Detect RPL-Based 6 LoWPAN Attacks?