# ADDAEIL: Anomaly Detection with Drift-Aware Ensemble-Based Incremental Learning
## 4 Results
### Model Performance Evaluation
Its reliance on a static, offline-trained model limits its adaptability to continuously evolving data streams, making it ill suited for scenarios with incremental drift where the data distribution changes gradually over time. Similarly, STAD employs a state-transition framework, where upon detecting concept drift, it either reuses an existing autoencoder or retrains a new one. Although this design allows adaptation to abrupt changes, it lacks the ability to perform continuous or incremental updates. During incremental drift, STAD continues to use outdated models until performance drops below a threshold, at which point it retrains a completely new model and discards prior information. This non-incremental updating makes STAD unsuitable for handling gradual, continuous data shifts characteristic of incremental drift. Both ADDAEIL and AEADF achieve high recall, with scores of 0.98 and 0.95. However, ADDAEIL reaches a higher precision of 0.75 compared to 0.54, leading to better overall performance. The key difference lies in their adaptation strategy. AEADF anchors its update decisions to a static reference model, which prevents timely adaptation to evolving data distributions. ADDAEIL uses performance-based evaluation and incrementally updates its model. This allows it to adapt more effectively as data evolves, maintaining reliability in non-stationary environments. At the same time, ADDAEIL demonstrates a strong capability to detect subtle anomalies within evolving data streams. On the Yahoo dataset, it achieved a precision of 0.75 and a recall of 0.98, both outperforming all other methods. The high precision demonstrates ADDAEIL's ability to accurately distinguish true anomalies from normal data fluctuations, effectively reducing false positives. Meanwhile, the high recall highlights its capacity to detect even subtle anomalies, minimizing false negatives and ensuring comprehensive coverage. Together, these results show that ADDAEIL effectively separates subtle anomalies from normal data, maintaining high detection accuracy even under incremental concept drifts. **NAB Dataset:** The NAB dataset includes both sudden and incremental drift. This makes it hard for models to react quickly while staying stable. In this setting, ECOD and LODA do not adapt well to the changing distribution. Their F1-scores are low, at 0.59 and 0.38. This shows that adaptive update mechanisms are necessary when facing drifts in data. MemStream also performs poorly, with an F1-score of only 0.0023. Its update process lacks structural guidance and is easily confused by sudden drift. Although it achieves a competitive AUC, this is misleading. NAB has a strong class imbalance, where anomalies make up only a small part of the data. In such cases, AUC alone often gives an overly optimistic view. F1, recall, and precision better reflect the model's real detection ability. ADDAEIL handles this setting much better. It achieves a strong F1-score of 0.61 and a precision of 0.58. This is due to its hybrid drift detection and update strategy, which adjusts based on drift intensity. By combining both distributional and structural detection results, ADDAEIL adapts well to both sudden and gradual drifts. It also avoids overreacting to small or temporary changes in the data. Among deep learning-based methods, LSTM-VAE and STAD demonstrative some competitive performance on the NAB dataset. LSTM-VAE achieves an F1-score of 0.62 and a recall of 0.97, suggesting its ability to capture anomalies during abrupt drift. This is mainly due to its use of LSTM to model temporal dependencies, combined with the forget gate, which discards irrelevant historical information. STAD achieves an F1-score of 0.57, a precision of 0.54, and a recall of 0.93. Its state-transition mechanism allows it to respond to sudden and severe drift by identifying changes and retraining a new autoencoder. However, this full retraining process discards prior information, limiting its long-term adaptability.