# ADDAEIL: Anomaly Detection with Drift-Aware Ensemble-Based Incremental Learning
## 4 Results
### Model Performance Evaluation  
ADDAEIL is evaluated against six representative benchmark methods covering a range of anomaly detection approaches, including static, semi-online, adaptive streaming, and deep learning-based methods. The key features of these methods are summarized in Table 1, and the results are presented in Table 2, which forms the basis for the following analysis. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Type** & **Drift-Aware** & **Incremental** & **Description** \\ \hline ECOD [16] & Offline & \(\times\) & \(\times\) & Static copula-based method. Common unsupervised benchmark. \\ \hline LODA [10] & Semi-Online, Ensemble & \(\times\) & \(\times\) & Random projection ensemble. Passive robustness to minor drift. \\ \hline MemStream [11] & Online & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) \\  & & Partial & Partial & Learers from recent data. \\ \hline LSTM-VAE [18] & Deep Learning & \(\times\) & \(\times\) & \(\times\) \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline STAD [22] & Deep Learning & \(\checkmark\) & \(\times\) & \(\times\) \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline AEADF [15] & Online, Ensemble & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) \\  & & Partial & Partial & drift. detector \\ \hline ADDAEIL (Proposed) & Online, Ensemble & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) \\  & & & & & \\  & & & & & \\ \end{tabular}
\end{table} 
Table 1: Overview of baseline methods used in Experiment Part I: Model Performance Evaluation. * **ECOD**[16]: A recent static anomaly detector based on empirical copulas. Although not tailored for streaming scenarios, ECOD is widely used as an unsupervised baseline and reflects the performance of non-adaptive methods under evolving conditions. * **LODA**[10]: A semi-online ensemble-based method that uses random projections and sparse histograms for lightweight anomaly scoring. While it does not update its model after deployment, it supports automatic bin sizing during training and offers some passive robustness to minor distributional changes. * **MemStream**[11]: A streaming anomaly detector that adapts to new data through a memory buffer mechanism. It can handle concept drift at the data level but lacks structural modeling of detector degradation. * **LSTM-VAE**[18]: A deep learning-based anomaly detector that employs an LSTM-based variational autoencoder for capturing temporal dependencies in time series data. Although capable of modeling complex sequential patterns, it requires offline training and lacks adaptation to evolving data distributions. * **STAD**[22]: An anomaly detection approach that applies KS and AD tests within the latent space of an autoencoder to capture subtle changes in data representation under concept drift. However, it does not monitor the performance of the model or internal structural alignment, and its sensitivity to drift is limited by the feature representation capabilities of the encoder. * **AEADF**[15]: An unsupervised ensemble-based anomaly detection framework for streaming time series with concept drift. It employs a hybrid drift detection mechanism combining histogram-based distributional comparison (e.g., Hellinger distance) and output-level evaluation. The framework uses a fixed update strategy anchored to a static initial model, without support for incremental model updates. This limits its adaptability to gradually evolving data streams. * **ADDAEIL (proposed)**: The proposed framework uses statistically grounded and structure-based drift detection, combined with an adaptive model update strategy. ADDAEIL enables incremental and interpretable adaptation in non-stationary streaming settings. Table 2 provides the detailed evaluation metrics that support the subsequent dataset-specific analysis.