Visible to the public A moving-target defense strategy for Cloud-based services with heterogeneous and dynamic attack surfaces

TitleA moving-target defense strategy for Cloud-based services with heterogeneous and dynamic attack surfaces
Publication TypeConference Paper
Year of Publication2014
AuthorsWei Peng, Feng Li, Chin-Tser Huang, Xukai Zou
Conference NameCommunications (ICC), 2014 IEEE International Conference on
Date PublishedJune
Keywordsattack-surface heterogeneity-and-dynamics awareness, attacker intelligence, cloud computing, cloud infrastructures, cloud-based service security, cloud-specific features, configuration staticity, deep automation, diversity-compatibility, dynamic attack surfaces, dynamic probability, Equations, heterogeneous attack surfaces, homogeneity problem, Information systems, Mathematical model, Moving-Target Defense, moving-target defense strategy, probabilistic algorithm, Probabilistic logic, probabilistic MTD service deployment, probability, Probes, replacement pool, risk modeling, S-shaped generalized logistic function, security, security of data, service attack surface, simulation, Uncertainty, VM migration-snapshotting

Due to deep automation, the configuration of many Cloud infrastructures is static and homogeneous, which, while easing administration, significantly decreases a potential attacker's uncertainty on a deployed Cloud-based service and hence increases the chance of the service being compromised. Moving-target defense (MTD) is a promising solution to the configuration staticity and homogeneity problem. This paper presents our findings on whether and to what extent MTD is effective in protecting a Cloud-based service with heterogeneous and dynamic attack surfaces - these attributes, which match the reality of current Cloud infrastructures, have not been investigated together in previous works on MTD in general network settings. We 1) formulate a Cloud-based service security model that incorporates Cloud-specific features such as VM migration/snapshotting and the diversity/compatibility of migration, 2) consider the accumulative effect of the attacker's intelligence on the target service's attack surface, 3) model the heterogeneity and dynamics of the service's attack surfaces, as defined by the (dynamic) probability of the service being compromised, as an S-shaped generalized logistic function, and 4) propose a probabilistic MTD service deployment strategy that exploits the dynamics and heterogeneity of attack surfaces for protecting the service against attackers. Through simulation, we identify the conditions and extent of the proposed MTD strategy's effectiveness in protecting Cloud-based services. Namely, 1) MTD is more effective when the service deployment is dense in the replacement pool and/or when the attack is strong, and 2) attack-surface heterogeneity-and-dynamics awareness helps in improving MTD's effectiveness.

Citation Key6883418