# Secure Aviation Control through a Streamlined ADS-B Perception System
## 3 Related Work
### Securing ADS-B
#### 3.2.3 Machine Learning Techniques  
This subsection will discuss the solutions that utilize different machine-learning techniques to build efficient detection models and the limitations of these proposed models. [PERSON] et al. [4], in their study, discussed the new attacks that the aircraft could be exposed to, such as false data injection, where the attacker could modify the contents of an ADS-B message and swindle other aircraft and sensors that rely on the ADS-B information. They proposed a set of algorithms that works towards not accepting any messages that contain any injection. However, this method still needs work, and modifications to ADS-B need to be applied to the devices on different aircraft. The authors of [31] carried out a study where they showcased multiple threats and vulnerabilities in what is considered the pillar of next-gen aircraft surveillance, which is currently being used among regions. The authors evaluated and showed the impacts of cyberattacks that utilized radio frequency to affect multiple networks. They implemented 12 attacks and only demonstrated five of them in their research. In total, the authors used 36 different ADS-B in message configurations (13 hardware devices and 22 software) and attacked them using BladeRF and HackRF as attacking devices; they achieved 90% accuracy in detecting spoofed messages and showed that their method could help distinguish the different types of ADS-B messages, which would help with mitigating some types of attacks. [PERSON] et al. [32] developed a novel method to detect global navigation satellite systems (GNSS) spoofing for aircraft equipped with ADS-B. Since the ADS-B equipment is mandated in civil aircraft in the US and surrounding areas by the Federal Aviation Administration, there was a need to address the GNSS spoofing. They proposed a model that would detect the spoofed signal by comparing the ADS-B inertial navigation system positions to the ones obtained by the spoofed GNSS. We summarize the most relevant solutions utilizing machine learning techniques to secure ADS-B in Table 4. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & \multicolumn{2}{c}{**Approach**} & \multicolumn{2}{c}{**Contributions**} & \multicolumn{2}{c}{**Limitations**} \\ \hline
[4] & \begin{tabular}{c} — \\ \end{tabular} & \begin{tabular}{c} Machine learning to block \\ injected messages. \\ \end{tabular} & \begin{tabular}{c} — \\ \end{tabular} & \begin{tabular}{c} Aircraft are exposed to false data \\ injection attacks. \\ \end{tabular} & \
\begin{tabular}{c} — \\ \end{tabular} &
\begin{tabular}{c} More work is needed to \\ improve the results, and \\ changes to the ADS-B protocol \\ must be implemented within \\ the different devices. \\ \end{tabular} \\ \hline
[31] & \begin{tabular}{c} — \\ \end{tabular} & \begin{tabular}{c} They performed attacks \\ and analyses to improve \\ machine \\ learning-based detection. \\ \end{tabular} & \begin{tabular}{c} — \\ \end{tabular} & \begin{tabular}{c} Performed 12 attacks but only \\ presented five in their paper. \\ Achieved 90\% accuracy in detecting \\ spoofed messages. \\ \end{tabular} & \
\begin{tabular}{c} — \\ \end{tabular} &
\begin{tabular}{c} Under what ML models were \\ used, and they acknowledged \\ that their model still requires \\ further improvements. \\ \end{tabular} \\ \hline
[32] & \begin{tabular}{c} — \\ \end{tabular} & \begin{tabular}{c} Detecting global \\ navigation satellite \\ systems spoofing. \\ \end{tabular} & \begin{tabular}{c} — \\ \end{tabular} & \begin{tabular}{c} Detect spoofed messages by \\ comparing the spoofed GNSS \\ messages with the locations from the \\ messages sent by the ADS-B sensors. \\ \end{tabular} & \
\begin{tabular}{c} — \\ \end{tabular} &
\begin{tabular}{c} It only works against GNSS \\ spoofing and can detect it based \\ on velocity and location. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the most relevant solutions utilizing machine learning techniques to secure ADS-B.