# Smart Cybersecurity Framework for IoT-Empowered Drones: Machine Learning Perspective
## 4 Experimental Assessment
### Incorporated Machine Learning Technique  
ML has made a significant contribution to the improvement of probabilistic ranking-based predictions. For rating classification, there are a plethora of ML classifiers. The Python Scikit-learn module contains a wide number of ML classifiers. It is an open-source library with a wide community of users. All classifiers in the current work, including gradient boosting model, stochastic gradient classifier, random forest, naive Bayes, extra tree classifier, and logistic regression, were implemented using the Scikit-Learn module. Each of these is discussed in detail ahead.  
1. _Random forest (RF)_ is an ensemble learning technique that uses decision trees (DT), which are also known as estimators, to classify data. The probabilistic ranking is  
Figure 12: BMP180 pressure sensor.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Attack Class** & **Type** \\ \hline DOS & land, back, pod, smurf \\ \hline R2L & ftp\_write, inap, multihop, phf, spy \\ \hline U2R & buffer-overflow, perl \\ \hline Probe & ipswep, portsweep \\ \hline \hline \end{tabular}
\end{table}
Table 5: Attack type and categories.  
used to combine the results of various trees to produce better results. The bagging technique is used to train trees using bootstrap samples. To evaluate the model's performance on test data, all trees are created in the same way. The decision tree with the lowest error rate is given a larger weight, resulting in a lower likelihood of making an inaccurate estimation.
2. _Decision tree (DT)_ is a multi-variable ML technique for text categorization that is widely used. It is used to forecast certain data values. It divides data into branching segments, which are utilized to construct an inverted tree with internal nodes, the root node, and leave nodes. The method is nonparametric, which means it can effectively assess complex data without imposing a sophisticated framework.
3. _Logistic regression_ is a mathematical strategy to predict outcomes; it examines data and works on numerous factors. It is a low-variance, basic but efficient approach that is commonly used in classification. This model may also be used to extract features. Stochastic gradient descent makes it simple to update with fresh data.
4. The _naive Bayesian classifier_, which makes independent assumptions among predictors, is based on [PERSON] theorem. It is simple to put together, with simple iterative parameter estimation. As a result, it is thought to be ideal for huge datasets. Despite its simplicity, it produces excellent results and outperforms other, more advanced classifiers.
5. In text categorization, the _support vector machine (SVM)_ is widely used. By maximizing the marginal distance, it draws hyperplanes that split classes. In binary classification, the SVM hyperplane separates the text into two separate groups. It is simpler and less complicated than deep learning approaches, and it is easy to understand. SVM is also commonly used to detect intrusions.
6. The _multilayer perceptron (MLP)_ is a basic deep learning model that can classify data rather well. It is a layered model, with neurons representing features in the initial layer and hidden layers processing incoming data and feeding them to the output layer represented by neurons. The numbers of hidden layers and neurons are chosen optimally to obtain the best outcomes. The model is trained using the hyperparameter values to increase classification training efficiency. Backpropagation, which is based on gradient descent, is commonly employed to assess MLP weights. The _rectified linear unit (ReLU)_ is employed in hidden layers, while the _sigmoid_ is utilized as an activation function. Sigmoid (G(x))= \(\frac{1}{(1+e^{-x})}\).  
The findings of several classifiers are pooled in ranking classifiers, and the ultimate choice is determined based on ranking.