Although the ability to model and infer attacker intent, objectives, and strategies (AIOS) may dramatically advance the literature of risk assessment, harm prediction, and predictive or proactive cyber defense, existing AIOS inference techniques are ad hoc and system or application specific. In this paper, we present a general incentive-based method to model AIOS and a game-theoretic approach to inferring AIOS. On one hand, we found that the concept of incentives can unify a large variety of attacker intents; the concept of utilities can integrate incentives and costs in such a way that attacker objectives can be practically modeled. On the other hand, we developed a game-theoretic AIOS formalization which can capture the inherent interdependency between AIOS and defender objectives and strategies in such a way that AIOS can be automatically inferred. Finally, we use a specific case study to show how attack strategies can be inferred in real-world attack--defense scenarios.

Although the ability to model and infer Attacker Intent, Objectives and Strategies (AIOS) may dramatically advance the literature of risk assessment, harm prediction, and predictive or proactive cyber defense, existing AIOS inference techniques are ad hoc and system or application specific. In this paper, we present a general incentive-based method to model AIOS and a game theoretic approach to infer AIOS. On one hand, we found that the concept of incentives can unify a large variety of attacker intents; the concept of utilities can integrate incentives and costs in such a way that attacker objectives can be practically modeled. On the other hand, we developed a game theoretic AIOS formalization which can capture the inherent inter-dependency between AIOS and defender objectives and strategies in such a way that AIOS can be automatically inferred. Finally, we use a specific case study to show how AIOS can be inferred in real world attack-defense scenarios.

Cloud computing has changed how services are provided and supported through the computing infrastructure. However, recent work [11] reveals that virtual machine (VM) colocation based side-channel attack can leak users privacy. Techniques have been developed against side-channel attacks. Some of them like NoHype remove the hypervisor layer, which suggests radically changes of the current cloud architecture. While some other techniques may require new processor design that is not immediately available to the cloud providers. In this paper, we propose to construct an incentive-compatible moving-targetdefense by periodically migrating VMs, making it much harder for adversaries to locate the target VMs. We developed theories about whether the migration of VMs is worthy and how the optimal migration interval can be determined. To the best of our knowledge, our work is the first effort to develop a formal and quantified model to guide the migration strategy of clouds to improve security. Our analysis shows that our placement based defense can significantly improve the security level of the cloud with acceptable costs.

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