Along with the importance of safety, an IDS has become a significant task in the real world. Prior studies proposed various intrusion detection models for the UAV. Past rule-based approaches provided a concrete baseline IDS model, and the machine learning-based method achieved a precise intrusion detection performance on the UAV with supervised learning models. However, previous methods have room for improvement to be implemented in the real world. Prior methods required a large labeling effort on the dataset, and the model could not identify attacks that were not trained before. To jump over these hurdles, we propose an IDS with unsupervised learning. As unsupervised learning does not require labeling, our model let the practitioner not to label every type of attack from the flight data. Moreover, the model can identify an abnormal status of the UAV regardless of the type of attack. We trained an autoencoder with the benign flight data only and checked the model provides a different reconstruction loss at the benign flight and the flight under attack. We discovered that the model produces much higher reconstruction loss with the flight under attack than the benign flight; thus, this reconstruction loss can be utilized to recognize an intrusion to the UAV. With consideration of the computation overhead and the detection performance in the wild, we expect our model can be a concrete and practical baseline IDS on the UAV.
|Title of host publication||Information Security Applications - 21st International Conference, WISA 2020, Revised Selected Papers|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||14|
|Publication status||Published - 2020|
|Event||21st International Conference on Information Security Applications, WISA 2020 - Jeju Island, Korea, Republic of|
Duration: 2020 Aug 26 → 2020 Aug 28
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||21st International Conference on Information Security Applications, WISA 2020|
|Country/Territory||Korea, Republic of|
|Period||20/8/26 → 20/8/28|
Bibliographical noteFunding Information:
This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2018-0-00232, Cloud-based IoT Threat Autonomic Analysis and Response Technology).
© Springer Nature Switzerland AG 2020.
- Intrusion detection system
- Unmanned aerial vehicle
- Unsupervised learning
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)