{\epsilon}-Indistinguishability In Moving Target Defense: Framework, Algorithms, And Cloud Case Studies
Researchers formalise a metric for moving target defence in cloud environments, quantifying how well configuration pools resist attacker fingerprinting — a gap most MTD implementations leave unaddressed.
Summary written by editorial AI · Source link below
arXiv:2607.13440v1 Announce Type: new Abstract: Moving Target Defense (MTD) assumes its pool of candidate configurations is safe to cycle among, i.e. latency and other observables do not trivially fingerprint the active choice, but this assumption has not been quantified at the pool level. We formalize this pool-safety problem as finding the largest $\varepsilon$-close subset of the Cartesian product of per-component implementation choices, reducing pairwise indistinguishability under an additi
Editorial Analysis
Enterprises adopting moving target defence in cloud workloads need assurance that configuration cycling actually raises attacker cost rather than merely adding operational complexity.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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