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

Title
UAV Anomaly Detection with Distributed Artificial Intelligence based on LSTM-AE and AE
Author
조인휘
Keywords
UAV; Anomaly detection; Intrusion detection; Scoring; LSTM-AE
Issue Date
2020-01
Publisher
Springer Verlag
Citation
Lecture Notes in Electrical Engineering, v. 590, page. 305-310
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.
URI
https://link.springer.com/chapter/10.1007%2F978-981-32-9244-4_43https://repository.hanyang.ac.kr/handle/20.500.11754/160962
ISBN
978-981-32-9244-4
ISSN
1876-1119; 1876-1100
DOI
10.1007/978-981-32-9244-4_43
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE(컴퓨터소프트웨어학부) > Articles
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