Enhanced Detection of Anomalous Network Behavior in Cloud - Driven Big Data Systems Using Deep Learning Models - Lin, Ying (Journal of Theory and Practice of Engineering Science, 2024)
Our study presents a deep learning-based approach to enhancing the detection of anomalous network behavior in cloud-driven Big Data environments. The proposed model was rigorously evaluated against traditional Intrusion Detection Systems (IDS) and other machine learning models, demonstrating superior performance with an accuracy of 98.7% and a recall rate of 96.7%. The model's precision, recorded at 95.4%, further underscores its capability to significantly reduce false positives, a common challenge in network security systems. These metrics not only highlight the model’s robustness in identifying both known and emerging threats but also affirm its scalability and effectiveness in real-time applications within complex cloud infrastructures. The study contributes to the field by offering a scalable solution that leverages the computational power of deep learning to address the growing complexity of network security in cloud environments. The model’s ability to process and analyze large-scale network traffic data with high precision and recall suggests a promising direction for future developments in AI-driven cybersecurity. Furthermore, the study addresses key challenges such as computational demands and data privacy concerns, providing insights into the practical deployment of such models in real-world settings.