DeaPS: Deep Learning-Based User-Level Proactive Security Auditing for Clouds
Minjie Ou, Liming Wang, Hao Xun · 2019
Auditing security compliance with respect to security standards and policies becomes increasingly important in clouds for ensuring the transparency and accountability of a cloud provider to its tenants. However, security auditing in clouds encounters various challenges in the scalability and response time due to the large-scale cloud size and the high operational complexity. Existing approaches cannot verify the legitimacy of user requests in proper response time at runtime for a large cloud. To this end, this paper proposes a novel security auditing framework named Deep lEArning-based user-level Proactive Security auditing (DeaPS) for clouds, which leverages the Long Short-Term Memory (LSTM) neural network to automatically learn user behavior patterns from historical events and notify for possible critical events causing violations. Our solution implements the costly verification in advance for reducing the runtime response time to a realistic level. We evaluate our approach by integrating DeaPS into OpenStack and extensive experiments show that DeaPS exhibits excellent performance in large-scale clouds and outperforms other existing security auditing methods.