In a time where general technology is progressing at a rapid rate, this thesis aims to present possible advancements to security in regard to air traffic communication. By highlighting how data can be extracted using simple hardware and open-source software the transparency and lack of authentication is showcased. The research is specifically narrowed down to discovering vulnerabilities of the ADS-B protocol in order to apply countermeasures. Through fetching live aircraft data with OpenSky-Network and through fetching simulated ADS-B attack data with OpenScope, this thesis develops a data set with both authentic and malicious ADS-B messages. The data set was cleaned in order to remove outliers and other improper data. A machine learning model was later trained with the data set in order to detect malicious ADS-B messages. With the use of Support Vector Machine (SVM), it was possible to produce a model that can detect four different types of aviation communications attacks as well as allow authentic messages to pass through the IDS. The finished model was able to detect incoming ADS-B attacks with an overall accuracy of 83.10%.