# ADDAEIL: Anomaly Detection with Drift-Aware Ensemble-Based Incremental Learning
## 4 Results  
### Experimental Setup  
We conducted four sets of experiments to comprehensively evaluate the performance of proposed framework under non-stationary streaming scenarios.  
Part I compares ADDAEIL with a variety of benchmark algorithms, including offline, semi-online, adaptive online methods, and deep learning-based models. Offline methods represent static detectors that do not respond to distributional changes. Semi-online methods support streaming inputs but lack explicit adaptation mechanisms. Adaptive online methods, by contrast, are capable of updating their models in response to evolving data. Deep learning-based models (e.g., LSTM-VAE and STAD) capture temporal dependencies or structural representations through complex architectures, typically requiring offline training. All the methods are evaluated under the same sliding-window protocol to ensure consistency.  
In the case of LSTM-VAE, we implemented a standard architecture with an encoder-decoder and RepeatVector structure, trained with Adam (learning rate = 0.001) and a mean squared error loss. For other methods (including STAD), we used recommended settings from the original papers or default values provided by standard packages.  
Part II presents an ablation study to assess the contribution of each core component in ADDAEIL. Three variants are tested against the proposed ADDAEIL framework:  
* **No Update**: A static iForest trained only once on the initial data \(T_{0}\).
* **Distribution-only**: Where drift detection and updates are only conducted based on the results of the KS and MWU tests in module 1. The replaced base detectors are randomly selected from the base model.
* **Performance-only**: Where updates are conducted based on the results of the path-length-based detector in module 2. The replacement ratio is set to be fixed with the value 0.3.
* **ADDAEIL (Proposed)**: The full version of the proposed framework, combining both drift detection results.  
Part III conducts a sensitivity analysis of three key hyperparameters: the sliding window size \(W\), the number of base detectors \(N\), and the update ratio range \([r_{\text{min}},r_{\text{max}}]\). Each parameter is varied independently to examine its effect on model performance, while all other settings are held constant.  
Part IV evaluates the practical deployment performance of the ADDAEIL framework in edge computing scenarios. To this end, we conducted experiments on the NVIDIA Jetson Orin NX 16 GB platform, measuring runtime efficiency and resource consumption on the CEMACS dataset. Metrics such as training time, per-window latency, maximum memory usage, and model size were collected to substantiate the framework's suitability for deployment in resource-constrained environments.  
In all four parts, the system is set to the fixed-size sliding-window setting. The first 30% of each time series is used as the initial training set \(T_{0}\) and the remaining 70% is processed in an incremental fashion in windows of size \(W=48\). The base model is initialized with \(N=100\) base detectors. The range of replacement ratio is set to be \([0.1,0.6]\).