Hardware Trojans (HTs) are gradually becoming a growing threat in the IoT landscape. This type of attack can result in catastrophic incidents for unmanned aerial vehicles (UAVs). Examples of these incidents could be information leakage, drone malfunction, which leads to crashes, and data integrity issues in information gathered by the sensors. Other papers have tried to resolve this issue by focusing on enhancing encryption and hardening the physical properties of the device to restrict information leakage. However, this research aims to demonstrate the efficacy of a side channel-based intrusion detection technique. This technique uses machine learning to detect HTs. We test this by constructing a PWM-inverting HT and implementing it into a UAV with a Pixhawk flight controller. By doing so, we demonstrate how this Intrusion Detection System (IDS) technique effectively detects incidents related to HT implementation on UAVs, analyzing discrepancies in the system’s impedance. Our proposed IDS yields ROC and accuracy scores up to 99.5% and 98%, respectively, in detecting HTs.