Seeking Black Lining In Cloud
This work addresses cloud security vulnerabilities for cloud providers and users, but it appears incremental as it applies a classification approach to a specific attack type.
The paper tackles the problem of detecting covert channel attacks that require time synchronization in cloud security by framing it as a binary classification task, achieving results based on features derived from Google cluster trace data without assuming data distribution.
This work is focused on attacks on confidentiality that require time synchronization. This manuscript proposes a detection framework for covert channel perspective in cloud security. This problem is interpreted as a binary classification problem and the algorithm proposed is based on certain features that emerged after data analysis of Google cluster trace that forms base for analyzing attack free data. This approach can be generalized to study the flow of other systems and fault detection. The detection framework proposed does not make assumptions pertaining to data distribution as a whole making it suitable to meet cloud dynamism.