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Human augmentation of UAV cyber-attack detection

Publication ,  Conference
Zhu, H; Elfar, M; Pajic, M; Wang, Z; Cummings, ML
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
January 1, 2018

Unmanned aerial vehicles (UAVs) have extensive applications in both civilian and military applications. Nevertheless, the continued development of UAVs has been accompanied by security concerns. UAV navigation systems are potentially vulnerable to malicious attacks that target their Global Positioning System (GPS). Thus, efficient GPS hacking detection with high success rate is paramount. Significant effort has been put into developing autonomous hacking detection techniques. However, little research has considered how a human operator can contribute to the security of such systems. In this paper, we propose a human-autonomy collaborative approach for a single operator of multiple-UAV supervisory control systems, where human geo-location is used to help detect possible UAV cyber-attacks. An experiment was designed and conducted using the RESCHU-SA experiment platform to evaluate this approach. The primary results show that 65% of all experiment sessions reached over 80% success rate in UAV hacking detection, while only 17% of participants lost one or more UAVs because of incorrect hacking detections. These results suggest that such an approach could help achieve better security guarantees for human-in-the-loop autonomous UAV systems that are prone to cyber-attacks.

Duke Scholars

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783319914664

Publication Date

January 1, 2018

Volume

10916 LNAI

Start / End Page

154 / 167

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

Citation

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Zhu, H., Elfar, M., Pajic, M., Wang, Z., & Cummings, M. L. (2018). Human augmentation of UAV cyber-attack detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10916 LNAI, pp. 154–167). https://doi.org/10.1007/978-3-319-91467-1_13
Zhu, H., M. Elfar, M. Pajic, Z. Wang, and M. L. Cummings. “Human augmentation of UAV cyber-attack detection.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10916 LNAI:154–67, 2018. https://doi.org/10.1007/978-3-319-91467-1_13.
Zhu H, Elfar M, Pajic M, Wang Z, Cummings ML. Human augmentation of UAV cyber-attack detection. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2018. p. 154–67.
Zhu, H., et al. “Human augmentation of UAV cyber-attack detection.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10916 LNAI, 2018, pp. 154–67. Scopus, doi:10.1007/978-3-319-91467-1_13.
Zhu H, Elfar M, Pajic M, Wang Z, Cummings ML. Human augmentation of UAV cyber-attack detection. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2018. p. 154–167.
Journal cover image

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783319914664

Publication Date

January 1, 2018

Volume

10916 LNAI

Start / End Page

154 / 167

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences