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

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.