# Secure Aviation Control through a Streamlined ADS-B Perception System
## 6 Conclusions and Remarks  
Automatic dependent surveillance-broadcast (ADS-B) is considered the future of aviation surveillance and aircraft traffic control. This protocol still shows that it lacks security. Researchers are still trying to improve the ADS-B security aspect to avoid risks such as causing collision avoidance system failure, reporting the wrong status of an aircraft, or even stealing the aircraft. Use of this technology is growing, but the data transmitted are unencrypted and have no actual method to authenticate, tempting hackers to exploit its flaws. We examined most of the prior research in the field for this study. Firstly, the literature addressed some key issues and vulnerabilities related to ADS-B technology. However, attacks still occur using low-cost equipment supported by technological advancement, creating new obstacles to overcoming these multiple risks. Secondly, this study aims to suggest a robust and efficient detection model for injected ADS-B messages to aid with the security of air traffic management and offer some answers to some of the challenges associated with identifying those messages and attacks related to them. The unique dataset has just over 11000 injected samples and 11000 benign samples, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Analysis Algorithm/Method** & **Accuracy** & **F1-Score** & **MCC** \\ \hline
[4] & Multiple machine learning models & 90\% & 92\% & - \\ \hline
[21] & Fingerprinting radio signals & 50\% & - & - \\ \hline
[38] & C-SVM & 95.32\% & - & - \\ \hline
[39] & Supervised deep learning & - & 98.46 & - \\ \hline Our model & Random forest & 99.41\% & 99.37\% & 0.988 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison with some of the related work. totaling 3 MB in size. A set of related features was chosen to identify the existing samples after the study's dataset had to be cleaned up before conducting any research because it obstructed the classification process. This experiment required us to test against six algorithms for better prediction results. We then compared the top three results from the binary classification stage. Thirdly, the results of our tests came as follows: random forest, multilayer perceptron classifier, and k-nearest neighbor, which, respectively, had an accuracy of 99.14%, 93.45%, and 96.50%. Additionally, random forest outperformed the multilayer perceptron classifier and k-nearest neighbor with an F1-score of 99.14% as opposed to 93.45% and 96.50%, respectively; and with an MCC of 0.982 as opposed to 0.870 and 0.930, respectively. Then, these models were further tested for multiclass classification, and since the dataset was imbalanced at this stage, three sampling techniques were used. The results came as follows: random forest, multilayer perceptron classifier, and support k-nearest neighbor, respectively, had an accuracy of 99.41%, 96.78%, and 94.80%. Additionally, random forest outperformed the multilayer perceptron classifier and k-nearest neighbor with an F1-score of 99.37% as opposed to 96.53% and 94.61%, respectively; and with an MCC of 0.988 as opposed to 0.925 and 0.843, respectively. While our research focuses on building and evaluating a standalone IDS to help improve the security of aviation control systems against injected cyberattacks (specifically: path modification, ghost aircraft, and velocity drift), the proposed IDS system needs to be integrated within the current aviation control infrastructures. The proposed sequence of events for implementing the detection system is to fine-tune the approach, integrate it with aviation systems, obtain regulatory approval, and then deploy and train the crew. However, several integration challenges might be faced; assuring data quality, accomplishing real-time processing, and reducing false alarms are among the challenges. Aviation security is improved by integration with current protocols; however, modifications are required for standardization, affordability, and scalability for broad implementation.