UAV Anomaly Detection with Distributed Artificial Intelligence Based on LSTM-AE and AE

Gimin Bae, Inwhee Joe

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Abstract

In this paper, we propose a novel method for UAV anomaly detection in the distributed artificial intelligence environment by using deep learning models. In the conventional artificial intelligence environment, a lot of computing power is required for anomaly detection, so it is not suitable to the UAV environment based on embedded systems. For UAV anomaly detection, distributed artificial intelligence with DPS (Distributed Problem Solving) and MAS (Multi-Agent System) is applied using LSTM-AE and AE models. The experimental results show that the proposed method performs well for anomaly detection in the UAV environment.

Original languageEnglish
Title of host publicationAdvanced Multimedia and Ubiquitous Engineering - MUE/FutureTech 2019
EditorsLaurence T. Yang, Fei Hao, Young-Sik Jeong, James J. Park
PublisherSpringer Verlag
Pages305-310
Number of pages6
ISBN (Print)9789813292437
DOIs
StatePublished - 2020 Jan 1
Event13th International Conference on Multimedia and Ubiquitous Engineering, MUE 2019 and 14th International Conference on Future Information Technology, Future Tech 2019 - Xian, China
Duration: 2019 Apr 242019 Apr 26

Publication series

NameLecture Notes in Electrical Engineering
Volume590
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference13th International Conference on Multimedia and Ubiquitous Engineering, MUE 2019 and 14th International Conference on Future Information Technology, Future Tech 2019
CountryChina
CityXian
Period19/04/2419/04/26

Fingerprint

Unmanned aerial vehicles (UAV)
Artificial intelligence
Multi agent systems
Embedded systems

Keywords

  • Anomaly detection
  • Intrusion detection
  • LSTM-AE
  • Scoring
  • UAV

Cite this

Bae, G., & Joe, I. (2020). UAV Anomaly Detection with Distributed Artificial Intelligence Based on LSTM-AE and AE. In L. T. Yang, F. Hao, Y-S. Jeong, & J. J. Park (Eds.), Advanced Multimedia and Ubiquitous Engineering - MUE/FutureTech 2019 (pp. 305-310). (Lecture Notes in Electrical Engineering; Vol. 590). Springer Verlag. https://doi.org/10.1007/978-981-32-9244-4_43
Bae, Gimin ; Joe, Inwhee. / UAV Anomaly Detection with Distributed Artificial Intelligence Based on LSTM-AE and AE. Advanced Multimedia and Ubiquitous Engineering - MUE/FutureTech 2019. editor / Laurence T. Yang ; Fei Hao ; Young-Sik Jeong ; James J. Park. Springer Verlag, 2020. pp. 305-310 (Lecture Notes in Electrical Engineering).
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abstract = "In this paper, we propose a novel method for UAV anomaly detection in the distributed artificial intelligence environment by using deep learning models. In the conventional artificial intelligence environment, a lot of computing power is required for anomaly detection, so it is not suitable to the UAV environment based on embedded systems. For UAV anomaly detection, distributed artificial intelligence with DPS (Distributed Problem Solving) and MAS (Multi-Agent System) is applied using LSTM-AE and AE models. The experimental results show that the proposed method performs well for anomaly detection in the UAV environment.",
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Bae, G & Joe, I 2020, UAV Anomaly Detection with Distributed Artificial Intelligence Based on LSTM-AE and AE. in LT Yang, F Hao, Y-S Jeong & JJ Park (eds), Advanced Multimedia and Ubiquitous Engineering - MUE/FutureTech 2019. Lecture Notes in Electrical Engineering, vol. 590, Springer Verlag, pp. 305-310, 13th International Conference on Multimedia and Ubiquitous Engineering, MUE 2019 and 14th International Conference on Future Information Technology, Future Tech 2019, Xian, China, 19/04/24. https://doi.org/10.1007/978-981-32-9244-4_43

UAV Anomaly Detection with Distributed Artificial Intelligence Based on LSTM-AE and AE. / Bae, Gimin; Joe, Inwhee.

Advanced Multimedia and Ubiquitous Engineering - MUE/FutureTech 2019. ed. / Laurence T. Yang; Fei Hao; Young-Sik Jeong; James J. Park. Springer Verlag, 2020. p. 305-310 (Lecture Notes in Electrical Engineering; Vol. 590).

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

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AB - In this paper, we propose a novel method for UAV anomaly detection in the distributed artificial intelligence environment by using deep learning models. In the conventional artificial intelligence environment, a lot of computing power is required for anomaly detection, so it is not suitable to the UAV environment based on embedded systems. For UAV anomaly detection, distributed artificial intelligence with DPS (Distributed Problem Solving) and MAS (Multi-Agent System) is applied using LSTM-AE and AE models. The experimental results show that the proposed method performs well for anomaly detection in the UAV environment.

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Bae G, Joe I. UAV Anomaly Detection with Distributed Artificial Intelligence Based on LSTM-AE and AE. In Yang LT, Hao F, Jeong Y-S, Park JJ, editors, Advanced Multimedia and Ubiquitous Engineering - MUE/FutureTech 2019. Springer Verlag. 2020. p. 305-310. (Lecture Notes in Electrical Engineering). https://doi.org/10.1007/978-981-32-9244-4_43