Deep Learning-Based Predictive Analytics for Anomaly Detection in Big Data Environments

With the increasing volume and complexity of data generated in diverse applications, anomaly detection in Big Data environments has become a critical task. This paper introduces a novel approach utilizing deep learning techniques to enhance predictive analytics for anomaly detection. We propose a hybrid model that integrates convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) to capture both spatial and temporal dependencies in large-scale datasets. The model is trained on labeled data to recognize normal patterns and subsequently detects anomalies through unsupervised learning. Experimental results on real-world Big Data sets demonstrate the effectiveness of our approach, outperforming traditional methods and showcasing the adaptability of deep learning in addressing the challenges posed by the dynamic nature of Big Data. This research contributes to the advancement of anomaly detection methodologies in complex, high-dimensional datasets and provides a foundation for developing robust systems capable of identifying irregularities in diverse Big Data applications.