Automatic Dependent Surveillance-Broadcast (ADS-B) is considered the future of aviation surveillance and traffic control as it allows different types of aircraft to transmit and gain information about their and other nearby aircraft's positions, velocity, and various other variables periodically. But, as this protocol still show that it lacks in terms of security and that researchers are still developing more methods and frameworks in order to secure this technology, we decided to join the initiative and propose an efficient detection method to help aid with detecting any attempts at injecting these messages which would cause multiple risks to aircrafts such as causing collision avoidance system failure, reporting wrong status of an aircraft, or even stealing it. This paper focused mainly on three different attacks; path modification, ghost aircraft injection, and velocity drift attacks. The dataset we utilized consisted of authentic messages captured from the OpenSky Network and generated injected messages using PyCharm. This study aims to provide a revolutionary methodology that, even in the face of new attacks (zero-day attacks), can successfully detect injected messages. The main advantage was utilizing a recent dataset to create more reliable and adaptive training and testing materials, which were then preprocessed before using different machine learning algorithms to feasibly create the most accurate and time-efficient model. The best outcomes of the binary classification were obtained with 99.14% accuracy, an F1-Score of 99.14%, and a Matthews' Correlation Coefficient (MCC) of 0.982. At the same time, the best outcomes of the multiclass classification were obtained with 99.41% accuracy, an F1-Score of 99.37%, and a Matthews' Correlation Coefficient (MCC) of 0.988. The dataset is thought to offer good outcomes, but the model still requires more testing and a bigger dataset, bearing in mind that the model still needs to be tested against other types of attacks.