Recently, methods to detect DoS and spoofing attacks on In-Vehicle Networks via the CAN protocol have been studied using deep learning models, such as CNN, RNN, and LSTM. These studies have produced significant results in the field of In-Vehicle Network attack detection using deep learning models. However, these studies have typically addressed studies on single-model intrusion detection verification in drone networks. This study developed an ensemble model that can detect multiple types of intrusion simultaneously. In preprocessing, the patterns within the payload using the measure of Feature Importance are distinguished from the attack and normal data. As a result, this improved the accuracy of the ensemble model. Through the experiment, both the accuracy score and the F1-score were verified for practical utility through 97% detection performance measurement.

Recently, methods to detect DoS and spoofing attacks that occur on in-vehicle networks using CAN Protocol are being studied through deep learning models such as CNN, RNN, and LSTM. These studies have produced significant results in the field of In-Vehicle Network attack detection using deep learning models. In addition, significant results are being achieved through research on applying time series-based deep learning models such as LSTM to detect DoS attacks and replay attacks occurring in in-drone networks by expanding them to drones using the UAVCAN protocol. In this paper, we conducted an experiment to detect in-drone network attacks through non-time series analysis using machine learning models and deep learning models, and through appropriate learning for each attack type, it can also be analyzed through non-time series analysis. The results showed that it was possible to detect attacks.