# ADDAEIL: Anomaly Detection with Drift-Aware Ensemble-Based Incremental Learning
## 4 Results
### Model Performance Evaluation
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multirow{2}{*}{Dataset} & \multicolumn{5}{c}{ECOD} & \multicolumn{5}{c}{LODA} & \multicolumn{5}{c}{MemStream} \\ \cline{2-11}  & **AUC** & **F1** & **Recall** & **Precision** & **AUC** & **F1** & **Recall** & **Precision** & **AUC** & **F1** & **Recall** & **Precision** \\ \hline Yahoo & 0.66 & 0.52 & 0.79 & 0.60 & 0.54 & 0.18 & 0.53 & 0.16 & 0.85 & 0.075 & 0.064 & 0.11 \\ NAB & 0.80 & 0.59 & 0.93 & 0.55 & 0.78 & 0.38 & 0.79 & 0.34 & 0.84 & 0.0023 & 0.004 & 0.016 \\ CEMACS & 0.91 & 0.82 & 0.81 & **0.92** & 0.90 & 0.78 & 0.75 & 0.93 & 0.84 & 0.73 & 0.86 & 0.73 \\ \hline
**Average** & 0.79 & 0.64 & 0.84 & 0.69 & 0.74 & 0.45 & 0.69 & 0.48 & 0.84 & 0.27 & 0.31 & 0.29 \\ \hline \multicolumn{11}{c}{LSTM-VAE} & \multicolumn{5}{c}{STAD} \\ \cline{2-11}  & **AUC** & **F1** & **Recall** & **Precision** & **AUC** & **F1** & **Recall** & **Precision** & & & \\ \hline Yahoo & 0.62 & 0.53 & 0.79 & 0.52 & 0.64 & 0.52 & 0.77 & 0.51 & & & & \\ NAB & 0.81 & **0.62** & **0.97** & 0.58 & 0.76 & 0.57 & 0.93 & 0.54 & & & \\ CEMACS & 0.92 & 0.88 & 0.88 & 0.93 & 0.91 & 0.88 & 0.86 & 0.96 & & & \\ \hline
**Average** & 0.97 & 0.68 & 0.88 & 0.68 & 0.77 & 0.66 & 0.65 & 0.67 & & & \\ \hline \multicolumn{11}{c}{AEADF} & \multicolumn{5}{c}{**ADDAEIL (Proposed)**} \\ \cline{2-11}  & **AUC** & **F1** & **Recall** & **Precision** & **AUC** & **F1** & **Recall** & **Precision** & & \\ \hline Yahoo & 0.82 & 0.58 & 0.95 & 0.54 & **0.92** & **0.77** & **0.98** & **0.75** & & & \\ NAB & 0.84 & 0.55 & 0.95 & 0.48 & **0.85** & 0.61 & **0.95** & **0.58** & & & \\ CEMACS & 0.93 & 0.88 & 0.87 & 0.94 & **0.94** & **0.90** & **0.92** & **0.92** & & & \\ \hline
**Average** & 0.86 & 0.67 & 0.92 & 0.65 & **0.90** & **0.76** & **0.95** & **0.75** & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison across benchmark datasets. Bold values indicate the best performance among all compared methods for each metric in each dataset. Yahoo Dataset: The Yahoo A3 dataset is challenging because it involves incremental concept drift. The data distribution changes gradually over time. It also contains some subtle anomalies, which are illustrated in Section 4.2. In this setting, static and semi-online methods like ECOD and LODA show clear weaknesses. ECOD is a non-adaptive static detector. It reaches an AUC of 0.66, but its F1-score of 0.52 and recall of 0.79 show reduced accuracy as the data evolves. LODA uses random projections and automatic binning. This gives it some resistance to small changes, but it cannot update itself. Its performance drops further, with an AUC of 0.54 and an F1-score of 0.18. These results show that static models do not work well when the distribution changes over time. To remain accurate and stable in non-stationary streams, models need to be able to update. MemStream is built for streaming data and updates based on memory. However, it does not model the drift detection. Its updates are unguided and often replace components ineffectively. This leads to unstable performance and an F1-score of 0.075. Updating based only on recent data is not enough when the model's internal structure no longer fits the current distribution. ADDAEIL addresses this by combining distribution-based testing with structure-based performance evaluation. It monitors path length distributions of base detectors to identify those most affected by drift. As a result, the model updates efficiently while preserving both accuracy and robustness. Both deep learning-based methods, LSTM-VAE and STAD, perform poorly on the Yahoo dataset, with F1-scores of 0.53 and 0.52 and precision scores of 0.52 and 0.51, respectively. Although LSTM-VAE leverages the ability of LSTM to capture temporal dependencies and selectively forget irrelevant historical information, combined with VAE's latent space modeling of data distributions, it lacks mechanisms to adapt to concept drift.