Communication systems in aviation tend to focus on safety rather than security. Protocols such as Automatic Dependent Surveillance-Broadcast (ADS-B) use plain-text, unauthenticated messages and, therefore, open to various attacks. The open and shared nature of the ADS-B protocol makes its messages extremely vulnerable to various security threats, such as jamming, flooding, false information, and false Squawk attacks. To handle this security issue in the ADS-B system, a state-of-the-art dataset is required to train the ADS-B system against these attacks using machine learning algorithms.