# Make Your Home Safe: Time-aware Unsupervised User Behavior Anomaly Detection in Smart Homes via Loss-guided Mask

arXiv: https://arxiv.org/abs/2406.10928 · HTML: https://mlsanity.com/paper/2406.10928/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 40 references that are in the map.
- First posted on arXiv 16 Jun 2024.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Behavior Sequence Transformer for E-commerce Recommendation in Alibaba](https://mlsanity.com/paper/1905.06874/) (2019)
- [Inducing High Energy-Latency of Large Vision-Language Models with Verbose Images](https://mlsanity.com/paper/2401.11170/) (2024)
- [BadCLIP: Trigger-Aware Prompt Learning for Backdoor Attacks on CLIP](https://mlsanity.com/paper/2311.16194/) (2023)
- [Backdoor Defense via Adaptively Splitting Poisoned Dataset](https://mlsanity.com/paper/2303.12993/) (2023)
- [Energy-Latency Manipulation of Multi-modal Large Language Models via Verbose Samples](https://mlsanity.com/paper/2404.16557/) (2024)
- [Accurate Action Recommendation for Smart Home via Two-Level Encoders and Commonsense Knowledge](https://mlsanity.com/paper/2208.06089/) (2022)
- [Aegis: A Context-aware Security Framework for Smart Home Systems](https://mlsanity.com/paper/1910.03750/) (2019)
- [ARGUS: Context-Based Detection of Stealthy IoT Infiltration Attacks](https://mlsanity.com/paper/2302.07589/) (2023)
- [Contextual Distillation Model for Diversified Recommendation](https://mlsanity.com/paper/2406.09021/) (2024)
- [6thSense: A Context-aware Sensor-based Attack Detector for Smart Devices](https://mlsanity.com/paper/1706.10220/) (2017)
- [IoTGaze: IoT Security Enforcement via Wireless Context Analysis](https://mlsanity.com/paper/2006.15827/) (2020)

## Built on by (most cited first, 5 of 5)

- [Video Watermarking: Safeguarding Your Video from (Unauthorized) Annotations by Video-based LLMs](https://mlsanity.com/paper/2407.02411/) (2024)
- [An Evidence-Based Post-Hoc Adjustment Framework for Anomaly Detection Under Data Contamination](https://mlsanity.com/paper/2510.21296/) (2025)
- [SmartBench: Evaluating LLMs in Smart Homes with Anomalous Device States and Behavioral Contexts](https://mlsanity.com/paper/2603.06636/) (2026)
- [Synthetic User Behavior Sequence Generation with Large Language Models for Smart Homes](https://mlsanity.com/paper/2501.19298/) (2025)
- [Tureis: Transformer-based Unified Resilience for IoT Devices in Smart Homes](https://mlsanity.com/paper/2603.10038/) (2026)

## Abstract (arXiv)

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.