Identity-based Security Enhancement in Cloud Computing: Deep Learning-Powered Threat Detection Framework

Cloud computing has improved data management but also increased security vulnerabilities, notably identity-based threats including identity spoofing, data breaches, and illegal access. In dynamic cloud systems, typical security methods cannot handle these evolving concerns, highlighting need for complex security measures. Deep learning-powered threat detection was established to improve cloud computing identity-based security. It uses LSTM and CNNs to analyze massive volumes of user behavior data and identify dangers. After training on dataset of many cloud simulation log entries, the framework combats many attack approaches. With false positive rate of less than 1% and 99% accuracy in recognizing identity-based threats, it beat standard machine learning models by 6% and dramatically reduced false warnings. This reduction is necessary to reduce security monitoring teams operating load. The framework processed data in 0.02 seconds per log entry to detect risks in real time. Overall, this deep learning-driven system improves identity-based threat detection in cloud security. Businesses looking to enhance the cloud security can benefit from its false positive rate and accuracy. The framework outperformed older techniques in accuracy by about 6%, proving its superiority in threat identification-based management. Future research could focus on refining model’s efficiency and expanding its relevance to more cloud-based environments.