The increasing reliance on unmanned aerial vehicles (UAVs) has escalated the associated cyber risks. While machine learning has enabled intrusion detection systems (IDSs), current IDSs do not incorporate cyber-physical UAV features, which limits their detection performance. Additionally, the lack of public UAV’s cyber and physical datasets to develop IDS hinders further research. Therefore, this paper proposes a novel IDS fusing UAV cyber and physical features to improve detection capabilities. First, we developed a testbed that includes UAV, controller, and data collection tools to execute cyber-attacks and gather cyber and physical data under normal and attack conditions. We made this dataset publicly available. The dataset covers a range of cyber-attacks including denial-of-service, replay, evil twin, and false data injection attacks. Then, machine learning-based IDSs fusing cyber and physical features were trained to detect cyber-attacks using support vector machines, feedforward neural networks, recurrent neural networks with long short-term memory cells, and convolutional neural networks. Extensive experiments were conducted on varying complexity and range of attack training data to explore whether (a) fusion of cyber and physical features enhances detection performance compared to cyber or physical features alone, (b) fusion enhances detection when IDS is trained on a single attack type and tested on unseen attacks of varying complexity, (c) fusion enhances performance when the range of attack training data increases and models are tested on unseen attacks. Answering these research questions provides insights into IDS capabilities using cyber, physical, and cyber-physical features under different conditions.

We investigated the following research questions in this paper:

• Will the fusion of cyber and physical features improve detection compared to cyber or physical features alone?
• Will the complexity level of the attack training data impact the ability of the IDS to detect unseen attacks with various complexities?
• Will the range of the attacks included in the training data impact the ability of the IDS to detect unseen attacks with various complexities?

• If you use our implementation, you are encouraged to cite our paper.
• This work is published in *IEEE Transactions on Intelligent Transportation Systems (2023).
• This work was supported by the National Science Foundation (NSF) Energy, Power, Control, and Networks Program (EPCN) under Award 2220346 *

@article{hassler2023cyber,
  title={Cyber-Physical Intrusion Detection System for Unmanned Aerial Vehicles},
  author={Hassler, Samuel Chase and Mughal, Umair Ahmad and Ismail, Muhammad},
  journal={IEEE Transactions on Intelligent Transportation Systems},
  year={2023},
  publisher={IEEE}
}