Optimizing Cloud Threat Detection with RCF, SVDD, CASB, and HBOS in Privacy-Centric Architectures

The explosive growth of cloud computing poses serious security concerns that require advanced threat detection systems. Traditional approaches are often lacking in scalability and privacy compliance. New techniques in real-time cloud threat detection include Random Cut Forest (RCF), Support Vector Data Description (SVDD), Cloud Access Security Broker (CASB), and Histogram-Based Outlier Detection (HBOS). The methodology proposed includes RCF, SVDD, CASB, and HBOS into privacy-centric designs. Each of these methods understands data distribution analysis, policy enforcement, and anomaly detection. If considered in combination, the above techniques can develop accurate real-time threat identification results. Their results are then aggregated in an ensemble-based categorization to identify risks with reliability and scalability but with privacy standards. The paper integrates RCF, SVDD, CASB, and HBOS into a privacy-centric framework in an effort to enhance the cloud environment through threat detection. Beyond the reduction in the cost of computation and the surety of data privacy, it has the eventual aim toward real-time detection of anomalies and establishment of an effective, scalable security solution for dynamic cloud environments. The integrated model surpassed the other more traditional methods such as PSO and HVMI in accuracy, precision, and F1-score (96.5%, 95%, and 94.8%, respectively). Due to its competitive computation time of 20 ms, it is ideal for real-time applications. For scalability and privacy maintenance in dynamic cloud environments, the proposed architecture was better compared to others. The hybrid model proposed will improve accuracy, real-time capability, and compliance with privacy while resolving cloud security issues. It will outperform the conventional techniques as it uses cutting-edge techniques that improve performance. This promotes safe and private designs by ensuring the detection of threats strongly for dynamic cloud environments.