MeNU: Memorizing normality for UAV anomaly detection with a few sensor values

Abstract

With advancements in unmanned aerial vehicle (UAV) technology, UAVs have become widely used across various fields, including surveillance, agriculture, and architecture. Ensuring the safety and reliability of UAVs is crucial to prevent potential damage caused by malfunctions or cyberattacks. Consequently, the need for anomaly detection in UAVs is rising as a preemptive measure against undesirable incidents. Therefore, UAV anomaly detection faces challenges such as a lack of labeled data and high system workload. In this paper, we propose MeNU, a lightweight anomaly detection system for UAVs that utilizes various sensor data to detect abnormal events. We generated a concise feature set through preprocessing steps, including timestamp pooling, missing-value imputation, and feature selection. We then employed MemAE, a variant of the autoencoder with a memory module that stores prototypical benign patterns, which is particularly effective for anomaly detection. Experimental results on the ALFA and UA datasets demonstrated MeNU’s superior performance, achieving AUC scores of 0.9856 and 0.9988, respectively, outperforming previous approaches. MeNU can be easily integrated into UAV systems, enabling efficient real-time anomaly detection.