Deep Learning Based Traffic Detection in Cloud Computing Environment

This work uses the characteristics of both architectures to present a novel hybrid model combining LSTM and GRU units to enhance anomaly detection in cloud environments. Separately integrating outputs from LSTM and GRU cells, a new fusion layer simultaneously captures long-term and short-term dependencies. Using attention mechanisms improves emphasis on pertinent sequence data, hence increasing detectability and interpretability. While a novel feature extraction technique emphasises important anomalies, adaptive learning rates customised to different model sections maximise learning efficiency. Custom loss functions improve sensitivity to small variations by punishing false negatives. The model distinguishes itself by being dynamically retrained on streaming data sets, therefore providing a self-adj usting mechanism for changing traffic patterns. Transparency in decision-making made possible by explain ability tools such as SHAP and LIME deepens anomaly detection. Genetic algorithms and reinforcement learning among other optimisation techniques help to fine-tune parameters, thereby improving accuracy and efficiency. Emphasising strong performance benchmarks, the model shows scalability and low-latency deployment in large-scale cloud systems. Novel methods of data augmentation replicate different traffic patterns, hence improving model adaptability and resilience. Combining creativity with pragmatic scalability for real-world use, this method offers a convincing, modern solution for difficult anomaly detection in cloud computing.