## 7 Result and Discussion
### RQ2: What Are the Existing ML-Based Approaches to Detect Attacks in RPL-Based 6 LoWPAN?
The overall results of the proposed model revealed that the proposed model obtained significant results of (97.14%, 97.03%, 0.36%, and 98%) in AC, Precision, FPR, and AUC-Receiver Operating Characteristic Curve (AUC-ROC) scores, respectively. Furthermore, the proposed MLRPL model gained better results than [49,58] approaches. Finally, it is worth mentioning that the proposed model is efficient in terms of training time and complexity of the ANN model. [PERSON] et al. [31] proposed a lightweight ML approach for detecting VN attacks in RPL-based IoT networks. The proposed approach, named ML-LGBM, used Gradient Boosting Machine for detecting the attacks. The methodology of the proposed approach implicates the production of an extensive dataset of VN attack, feature extraction module, LGBM-based classification algorithm, and optimization of the model parameters. Furthermore, the author employed several stages to reach the desired result to detect the attack mentioned above. Such stages encompass design of the RPL network, data collection, data preprocessing, feature selection, and the ML model. Moreover, the authors store the collected data using different readable file formats in the data collection stage. After that, the data preprocessing stage prepares the data for the feature selection stage, which involves a Forward Feature Selection (SFS) method to extract the optimal subset of features. The outcome of this stage resulted in 11 features selected from 17 features. After that, the selected features from the previous stage are classified using ML-LGBM to detect the normal and malicious behavior. The results show that the ML-LGBM model attained 99.6%, 99%, 99.6%, 99.3%, and 0.0093 of AC, precision, F-Score, True Negative Rate (TNR), and FNR, respectively. Furthermore, the author compared the performance of their approach in terms of (Training Time, Testing Time, Model Size, AC, Precision, FPR, and F-score) with several existing studies, and the proposed model outperformed other approaches. Additionally, as for the consumption of the system's resources, the model achieved 140.217 seconds execution time and 347,530 bytes of memory size. An ML approach for securing RPL routing (MLRP) protocol was introduced by [59]. In this work. the authors targeted several RPL-based attacks, namely VN, Rank, and DoS attacks. The MLRP approach comprises two stages: Data Generation and Attack Detection. The former stage focuses on generating the dataset, which involves creating normal and attack scenarios, while the latter stage, called ML with SVM classifier, includes several modules, namely feature extraction, feature selection, labeling, and attack detection. In addition, during the feature extraction module, the authors extracted the features from PCAP files and applied some preprocessing steps to the collected features. After that, the authors used Principal Component Analysis (PCA) to reduce the number of features and select significant features that contribute to attack detection. Finally, the classification module used SVM to identify and classify RPL attacks based on the training step. The experimental results show that the average AC obtained by MLPR is between 0.90 and 0.92. Moreover, the Recall metric gains between 0.96 and 0.98. Furthermore, the proposed approach reveals 76.8% of PDR compared to other existing RPL-based approaches when using 1474 control messages with 30 network nodes. [PERSON] et al. [60] carried out a routing method for the security of RPL networks using the Ant Colony Optimization (ACO) algorithm. The proposed method dealt with the destination nodes as the root node and used the ACO algorithm to improve the security and reliability of the routing process. In this work, the authors deployed the ant of the ACO algorithm at regular intervals in each node to find the best path to the destination node. After that, each node ant finds the next step using the probability formula. Then, the node selects the route with the highest probability.