Learning Latent Representation with Limited Labels for IoT Anomaly Detection

Abstract

Abstract— Malware detection is a critical challenge in the current era, especially for IoT devices. Previous studies have applied analytic techniques to reduce data size and extract valuable information. However, most of these studies count on a considerable quantity of outliers to perform anomaly detection. In this paper, we propose an enhanced method (named FeaWAD*) that improves the data encoding strategy based on the FeaWAD network [1]. These models require only a small fraction of anomalies for training. We evaluate the FeaWAD* method on the N-BaIoT dataset with various test scenarios for detecting known attacks as well as unknown future attacks. The experimental results demonstrate that the FeaWAD* method outperforms the original model FeaWAD and other popular anomaly detection methods such as Isolation Forest, Local Outlier Factor, and One-class Support Vector Machine. We also measure the time-based effectiveness of the model to assess its practical applicability.