Analyzing Suspicious Behaviour in Automatic Tracking of Aircraft Data Using Machine Learning and Deep Learning

Ensuring the security and reliability of the Automatic Dependent Surveillance–Broadcast (ADS-B) system is essential for modern aviation safety. This research enhances its resilience by drawing on recent advancements in machine learning as well as deep learning, resulting in a robust detection mechanism. The system processes aircraft transmission data to identify deviations in ADS-B parameters and classify them into three categories: normal, potential anomaly, and confirmed anomaly. This classification enables early identification of irregular patterns that may indicate potential threats or abnormal operational behaviour, helping to mitigate faults or disruptions before they escalate. By employing advanced detection algorithms, the proposed approach strengthens ADS-B system security, supporting safer and more dependable air traffic operations.