A Deep Learning-Driven Framework for Detecting Anomalous Data Breaches in Distributed Cloud Storage Infrastructures

The emergence of cloud storage systems and infrastructures has necessitated some new issues associated with data integrity, security and privacy management. The distributed cloud settings, being resilient and scalable, are especially prone to various types of cyberattacks, including anomalous data loss. Intuitive intelligence in providing real-time security detectors plays a pivotal role since traditional security mechanisms do not meet this aspect in most cases because of the dynamic and decentralized aspects of such infrastructures. This paper suggests a sound deep learning based system to identify an unusual data breach efficiently in the distributed cloud storage facilities. Our framework uses a hybrid of Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and Autoencoders in order to analyze huge volumes of log data and metadata, which is produced by the cloud systems. We provide an overview of the whole detection system architecture, with pre-processing pipelines, anomaly scoring modules, and real-time alerting modules. A large-scale experiment was done on publicly available datasets and our own generated datasets, which represent cloud data breach scenarios. The proposed model achieved a detection accuracy of 98.7% and a false positive rate of 1.2%, outperforming the current state-of-the-art methods. Moreover, the structure is scalable, flexible and capable of being combined with diverse cloud service providers. This paper describes the proposed system in terms of its theoretical basis, implementation methods, and empirical assessment