Adaptive Deep Learning Framework with Unicintus Optimization for Anomaly Detection in Streaming Data

Anomaly detection in streaming data is crucial for identifying unusual patterns or outliers that may indicate significant issues. Traditional methods struggle with the inability in efficiently handling high-velocity data, adapting to changing data distributions, and maintain performance over time. Further, the conventional methods struggled with scalability, adaptability, and computational efficiency, leading to delays in detection or an increased rate of false positives. To address these limitations, Unicintus Escape Energy enabled Sampling based Drift Deep Belief Network-Bidirectional Long Short Term Memory (UES2-DTM) is proposed in the research. The research model incorporates the combination of adaptive reservoir sampling as well as the adaptive sliding window mechanisms into the base model, which elevates the efficiency of the model to work with the streaming data. Moreover, the adaptive sliding window mechanisms for drift detection integrates the Unicintus Escape Energy Optimization (UE2O) Algorithm to boost efficiency by dynamically adjusting the sliding window size and parameters, based on real-time streaming data characteristics. Further, Adaptive reservoir sampling helps in maintaining a representative sample of the data stream, for effective detection. Overall, the UES2-DTM model demonstrates superior adaptability and accuracy, which is evaluated with the metrics such as precision, recall, F1-score, and Mean Square Error (MSE) attained 97.199%, 94.827%, 95.998%, and 3.461 respectively.