# Smart Cybersecurity Framework for IoT-Empowered Drones: Machine Learning Perspective
## 4 Experimental Assessment
### Statistical Analysis  
The statistical findings in terms of performance parameters of accuracy, precision, recall, and F1-measure are discussed in the current section. The presented framework's results are compared with state-of-the-art ML models that were used on the drone dataset. Explicitly, decision tree, random forest, multilayer perceptron, logistic regression, Naive Bayes, support vector machine, and random forest are the incorporated classifiers. All of the tests were carried out in Python using _Keras, SciKit-learn_, and _Tensorflow_. Data were divided into three categories of _jamming, spoofing, and denial-of-service attacks_. Table 6 demonstrates the detailed computation results of statistical parameters. The proposed technique acquired  
Figure 13: Temporal Delay. 98.58% accuracy, which is the highest accuracy score for threat detection in comparison to other techniques. This shows that the presented technique is optimal in the detection of attacks and classifying data instances in real time. Moreover, for precision analysis, the presented model was able to acquire the maximal value of 97.68% in comparison to state-of-the-art ML techniques. Moreover, logistic regression was the next ML which obtained the second-best precision measure of 96.25%. This can be deduced from the fact that the presented model is made of hybrid logistic regression, which has influenced precision computation. Similarly, for recall computation, it can be seen that the presented model attained an elevated value of 98.59%, which is comparatively better than ML other models in the current scenario. It depicts that the presented technique has the ability to estimated correct data instances which are vulnerable to attacks in the IoT-UAV scenario. Likewise, for the estimation of the F-measure, the presented model was able to register a maximal value of 99.01%, which shows the enhanced performance. Comprehensively, it can be stated that in the current scenario, the presented technique is statistically better. Figure 14a-d depict the detailed results for statistical measures for a variable number of instances. Moreover, it can be seen that the performance of the comparative ML technique is decreasing with an increasing number of instances. This is because the conventional ML technique uses the statistical approach for performance estimation. However, the presented techniques use iterative techniques to minimize the error in the computation. The iterative behavior ensures optimization of the computation. Moreover, with the increased number of data instances, the training accuracy of the proposed model increases, which increases the accuracy in the case of large data instances. In addition to the aforementioned results, the presented model was compared for performance assessment for different datasets. The Performance analysis can be found in Table 7. \begin{table}
\begin{tabular}{c c} \hline
**Category** & **Detail** \\ \hline Normal & Connections are generated by simulating user behavior. \\ \hline DoS attacks & Use of resources or services are denied to authorized users. \\ \hline Probe attack & Information about the system is exposed to unauthorized entities. \\ \hline User to remote attacks & Access to account types of administrator is gained by unauthorized entities. \\ \hline Remote to local attacks & Access to hosts is gained by unauthorized entities. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Dataset classes. \begin{table}
\begin{tabular}{c c c c c} \hline
**Models** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline Random forest & 92.36 & 92.36 & 93.15 & 94.56 \\ \hline Decision tree & 93.25 & 91.26 & 93.25 & 95.62 \\ \hline Logistic regression & 92.23 & 96.25 & 94.15 & 96.32 \\ \hline Naive Bayes & 89.65 & 90.47 & 91.25 & 92.25 \\ \hline Support vector machine & 92.36 & 94.26 & 93.25 & 92.25 \\ \hline MLP & 89.65 & 88.14 & 89.25 & 92.15 \\ \hline Proposed & 98.58 & 97.68 & 98.59 & 99.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance analysis.