Masked Memory Network for Semi-Supervised Anomaly Detection in Internet of Things

With the rapid development of Internet of things (IoT), an increasing volume of data is generated across diverse IoT devices. Within these data, an extremely limited subset may manifest notable deviations from the majority of data, such as network intrusion data in traffic monitoring devices. The identification of such anomalous data assumes considerable importance across diverse domains. In this paper, we propose Masked Memory Network, a semi-supervised anomaly detection method which can be applied to various IoT devices. Our approach leverages a masked memory module combined with a soft masking strategy to acquire discriminative patterns capable of distinguishing anomalies from normal data. We also devise a anomaly scoring strategy which can exploits the characteristics of attention weights between test samples and memory items to detect both known and unknown anomalies. Experimental results on various AD datasets collected by IoT devices demonstrate the effectiveness of our work in numerous IoT applications.