Intrusion Detection on Unmanned Aerial Vehicle (UAV) using Binary Decision Tree

Intrusion Detection holds paramount significance in the realm of Unmanned Aerial Vehicle (UAV) operations, necessitating the deployment of robust and effective system methodologies. Among these methodologies, Binary Decision Tree stands out as a prominent technique within Intrusion Detection. With this context in mind, I aim to present the outcomes of my research pertaining to Intrusion Detection on UAVs, employing the Binary Decision Tree approach. The Binary Decision Tree method entails the integration of an algorithm into Machine Learning, which subsequently executes the algorithmic process. The findings derived from the UAV Intrusion Detection, employing the Decision Tree methodology, have demonstrated a remarkable level of accuracy, achieving a perfect score of 100% across all evaluated aspects within the datasets. In the testing phase, involving six distinct datasets, only two displayed aspect values slightly below 100%.