Evaluating the Security and Economic Effects of Moving Target Defense Techniques on the Cloud
This work addresses cloud security and cost-effectiveness for applications like E-health, but it is incremental as it builds on existing MTD methods with new evaluations.
The paper tackled the lack of joint evaluation of Moving Target Defense (MTD) techniques for cloud security by mathematically combining three techniques and assessing them using security and economic metrics, finding optimized deployments that maximize net benefit in E-health cloud models.
Moving Target Defense (MTD) is a proactive security mechanism which changes the attack surface aiming to confuse attackers. Cloud computing leverages MTD techniques to enhance cloud security posture against cyber threats. While many MTD techniques have been applied to cloud computing, there has not been a joint evaluation of the effectiveness of MTD techniques with respect to security and economic metrics. In this paper, we first introduce mathematical definitions for the combination of three MTD techniques: \emph{Shuffle}, \emph{Diversity}, and \emph{Redundancy}. Then, we utilize four security metrics including system risk, attack cost, return on attack, and reliability to assess the effectiveness of the combined MTD techniques applied to large-scale cloud models. Secondly, we focus on a specific context based on a cloud model for E-health applications to evaluate the effectiveness of the MTD techniques using security and economic metrics. We introduce (1) a strategy to effectively deploy Shuffle MTD technique using a virtual machine placement technique and (2) two strategies to deploy Diversity MTD technique through operating system diversification. As deploying Diversity incurs cost, we formulate the \emph{Optimal Diversity Assignment Problem (O-DAP)} and solve it as a binary linear programming model to obtain the assignment which maximizes the expected net benefit.