Real-Time Data Stream Anomaly Detection Using an Adaptive Multi-Model Ensemble Approach

This study proposes a novel hybrid ensemble architecture for real-time anomaly detection in high-velocity data streams, integrating complementary machine learning paradigms to address accuracy, latency, and adaptability constraints. The framework combines an Isolation Forest model for rapid unsupervised outlier scoring, a Long Short-Term Memory (LSTM) autoencoder for temporal–frequency sequence reconstruction, and an ADWIN-based concept drift detector to enable dynamic window resizing and adaptive learning. Real-time anomaly detection plays a critical role in promptly identifying irregular patterns across domains such as cybersecurity intrusion detection, industrial equipment monitoring, smart grid analytics, and financial fraud detection. To emulate realistic streaming environments, a distributed ingestion pipeline built on Apache Kafka is implemented to simulate continuous data flow using benchmark datasets, including the Numenta Anomaly Benchmark (NAB) and the KDD Cup 1999 network traffic dataset. This setup enables systematic evaluation under varying anomaly injection rates, noise levels, and feature dimensionalities. For each streaming window, multi-domain feature extraction is performed, encompassing statistical descriptors (mean, variance, kurtosis), temporal dynamics (lag correlations, trend coefficients), and frequency-domain characteristics obtained via spectral analysis. The resulting high-dimensional feature vectors are projected into a compact latent space using incremental Principal Component Analysis (PCA) to reduce computational overhead while preserving discriminative information. The ensemble aggregates submodel anomaly scores through dynamically adaptive weighting, where weights are recalibrated based on recent predictive performance and drift detection signals. This strategy enhances robustness against evolving data distributions and mitigates performance degradation under concept drift. Extensive experimental evaluation demonstrates that the proposed framework achieves an average F₁-score of 93.0%, outperforming standalone Isolation Forest and LSTM autoencoder baselines by approximately 7% and 6%, respectively. Furthermore, the system maintains sub-second inference latency, with mean detection delays of 28.4 ms on server-grade hardware and 112.1 ms on resource-constrained edge devices. Resource utilization profiling indicates efficient CPU consumption (