Evaluating hyperparameter transferability in unsupervised anomaly detection for smart home environments

Deploying unsupervised anomaly detection systems in heterogeneous smart home environments is hindered by the need for costly, per-site hyperparameter tuning. This paper addresses the critical challenge of hyperparameter transferability for creating zero-tune, plug-and-play security solutions. We systematically evaluate five unsupervised machine learning models [Elliptic Envelope (EE), Isolation Forest (IF), Local Outlier Factor (LoF), One-Class SVM (oSVM), and an Autoencoder (AE)] across five prominent IoT datasets. Using a rigorous dataset-specific hyperparameter tuning approach, we benchmark the performance of transferred configurations against both per-dataset optimization and default settings. Our findings establish a clear performance hierarchy: while dataset-specific tuning remains the gold standard, an intelligent transfer strategy significantly outperforms default configurations. Notably, we identify the IoTID20 dataset as the most effective source. Our quantitative topological analysis supports this, revealing that IoTID20 s high feature space complexity and cluster overlap (evidenced by low Silhouette scores) create a rigorous training environment that produces robust, portable hyperparameters. Furthermore, our analysis reveals a strategic trade-off: Autoencoders and LoF deliver the highest absolute performance, whereas IF offers the most substantial improvement over default settings. This work provides a quantitative framework for dataset-driven initialization, guiding the development of robust, low-maintenance intrusion detection systems.