Design a Security Framework for IoT Security.
The implementation of ML and DL algorithms for enhancing the security of IoT systems.
To conduct this research, you are required to use at least three ML and three DL algorithms and apply them to two publicly available datasets for IoT security. Additionally, it is crucial to evaluate the performance of these algorithms using all metrics such as
1. Accuracy: Measures the proportion of correctly classified instances in the dataset.
2. Precision: Measures the proportion of true positives among all instances classified as positive by the algorithm.
3. Recall Measures the proportion of true positives among all instances that are actually positive in the dataset.
4. F1 score: Harmonic mean of precision and recall.
5. Area Under the Receiver Operating Characteristic curve (AUC-ROC): Measures the performance of a binary classification algorithm by varying its threshold value.
6. Mean Squared Error (MSE): Measures the average squared difference between the predicted and actual values in a regression problem.
7. Mean Absolute Error (MAE): Measures the average absolute difference between the predicted and actual values in a regression problem.
8. Confusion Matrix: A table that summarizes the performance of a classification algorithm by comparing the predicted and actual labels.
Moreover, you must compare the performance of each algorithm and present their findings with appropriate statistical analysis.
Also Use the following graphs to validate the results
1. Receiver Operating Characteristic (ROC) Curve: A plot of the true positive rate against the false positive rate for varying threshold values of a binary classification algorithm.
2. Precision-Recall Curve: A plot of precision against recall for varying threshold values of a binary classification algorithm.
3. Confusion Matrix Heatmap: A visual representation of the performance of a classification algorithm using a heatmap that shows the number of instances in each cell of the confusion matrix.
4. Learning Curves: Plots of the training and validation performance of an algorithm as a function of the number of training examples.
5. Loss Curves: Plots of the training and validation loss of an algorithm as a function of the number of training iterations.
6. Feature Importance Plot: A graph that shows the importance of each feature used in the ML or DL algorithm to make predictions.
7. Residual Plot: A graph that shows the difference between the predicted and actual values in a regression problem.
