## 7 Result and Discussion
### RQ2: What Are the Existing ML-Based Approaches to Detect Attacks in RPL-Based 6 LoWPAN?
Additionally, the authors implemented their approach on the IoT testbed, and the results revealed the Precision value (98.47%) and Recall value (97%) using actual sensors. [PERSON] and [PERSON] [1] proposed a mitigation approach for minimizing the effect of IoT routing attacks on PRC in the healthcare environment. The proposed work uses a Convolution Neural Network (CNN) to discover five routing attacks: HF, SF, SH, WH, and VN. The architecture of the proposed approach comprises three layers, the medical data collection layer, the routing and network layer, and the medical application layer. In addition, the authors utilized a real-time dataset generated using the Cooja simulator. Finally, the Synthetic Minority Over-sampling TEhnique (SMOTE) was applied to deal with unbalanced samples in the generated dataset. The authors employed three feature selection methods during the preprocessing step: weight by One Rule (One-R), chi-square, and weighted random Forest. Such methods are also used to solve other issues, such as noise and over-fitting. Then, they implemented the CNN model to identify suspicious behavior in network traffic. As for the experimental result, the results show that the proposed approach attained 96.87%, 94.85%, 3.13%, 99.65%, 93.8%, 97.19%, and 0.325 for AC, Precision, Error Rate, Recall, Correlation, F-measure, and Logistic Loss, respectively, for HF attacks. Overall, the proposed approach shows significant outcomes in terms of AC, Precision, Correlation, Recall, Error Rate, and Logistic Loss, leading to a reduction in PRC. A DL approach for detecting Rank attack in RPL was introduced by [62]. The proposed approach employs MLP for verifying and classifying the normal and suspicious behavior of network traffic. In their work, the authors self-generated their dataset using the Cooja simulator. Moreover, during the implementation of the RPL attack in the data collection stage, the authors modified a set of parameters in the simulator's configuration, such as icmpvo and dag rpl files. Such modifications are necessary to initialize the RPL attack. In addition, the authors applied some preprocessing techniques, such as feature extraction, binarization, normalization, and sampling, to obtain a readable data format that are useable for training the MLP algorithm and testing the produced model. Furthermore, throughout the creation of the MLP model, the authors used various inputs and outputs from the dataset. Moreover, various functions and parameters were used during the MLP implementations stage, such as ReLU being assigned as an activation function. To evaluate the proposed approach, the authors split the dataset into 80% and 20% for the training and testing phases, respectively. Regarding the experimental result, the proposed approach obtained 100% TPR and 24% FPR. Additionally, the model achieved 98%, 88%, and 92% for Precision, Recall, and F1-score, respectively, on the outcome of macro-Avg and weighted-Avg. Similarly, in terms of AC, the proposed approach attained a high performance of up to 96%. [PERSON] et al. [65] developed a holistic framework to predict routing attacks in IoT-LLNs. They used several DL tools, such as the Graph Convolution Neural Network (GCNN) and Long Short-Term Memory (LSTM) model, to capture the spatial and temporal features of the IoT-LNNs. Furthermore, the authors used the smart contract-fortified blockchain technique to preserve the collected data from network nodes. Meanwhile, in the event of any suspicious behavior identified, such as the change of node's rank exceeding the assigned threshold, the VN created by a non-sink node, and the number of DIS messages exceeding the pre-defined threshold, the smart contract generates a warning impulse. Another role of the smart contract is to obtain the required feature extraction and initialize the dataset and data visualization tool. Finally, the FNN model utilized the output from LSTM, GCN, and warning impulse from the blockchain-based smart contract for attack predictions.