Unsupervised intrusion detection system for unmanned aerial vehicle with less labeling effort

Kyung Ho Park, Eunji Park, Huy Kang Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationInformation Security Applications - 21st International Conference, WISA 2020, Revised Selected Papers
EditorsIlsun You
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages14
ISBN (Print)9783030652982
Publication statusPublished - 2020
Event21st International Conference on Information Security Applications, WISA 2020 - Jeju Island, Korea, Republic of
Duration: 2020 Aug 262020 Aug 28

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12583 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference21st International Conference on Information Security Applications, WISA 2020
Country/TerritoryKorea, Republic of
CityJeju Island

Bibliographical note

Funding 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).

Publisher Copyright:
© Springer Nature Switzerland AG 2020.


  • Autoencoder
  • Intrusion detection system
  • Unmanned aerial vehicle
  • Unsupervised learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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