  Smart homes, powered by the Internet of Things, offer great convenience but
also pose security concerns due to abnormal behaviors, such as improper
operations of users and potential attacks from malicious attackers. Several
behavior modeling methods have been proposed to identify abnormal behaviors and
mitigate potential risks. However, their performance often falls short because
they do not effectively learn less frequent behaviors, consider temporal
context, or account for the impact of noise in human behaviors. In this paper,
we propose SmartGuard, an autoencoder-based unsupervised user behavior anomaly
detection framework. First, we design a Loss-guided Dynamic Mask Strategy
(LDMS) to encourage the model to learn less frequent behaviors, which are often
overlooked during learning. Second, we propose a Three-level Time-aware
Position Embedding (TTPE) to incorporate temporal information into positional
embedding to detect temporal context anomaly. Third, we propose a Noise-aware
Weighted Reconstruction Loss (NWRL) that assigns different weights for routine
behaviors and noise behaviors to mitigate the interference of noise behaviors
during inference. Comprehensive experiments on three datasets with ten types of
anomaly behaviors demonstrates that SmartGuard consistently outperforms
state-of-the-art baselines and also offers highly interpretable results.
