DJB 2016: "Since operational MTD by its nature introduces changes with system-wide information dissemination requirements (eg if you mutate service mapping to confuse the bad guys, the good guys still need to know the new mappings), the MTD modeling should be informed by concepts addressing these issues. Specifically, we recommend framing

[..] 3. MTD as asymmetric adversarial learning, with focus on information leaks and emitted side channels. We offer [64] as a starting point to quantitatively assess general information leakage bounds robust with respect to operational scenarios."

2021:"Space systems provide many critical functions to the military, federal agencies, and infrastructure networks. [..] We devised a MTD algorithm and tested its application to a real-time network. We demonstrated MTD usage with a real-time protocol given constraints not typically found in best-effort networks. Second, we quantified the cyber resilience benefit of MTD given an exfiltration attack by an adversary. For our experiment, we employed MTD which resulted in a reduction of adversarial knowledge by 97%. Even when the adversary can detect when the address changes, there is still a reduction in adversarial knowledge when compared to static addressing schemes. Furthermore, we analyzed the core performance of the algorithm and characterized its unpredictability using nine different statistical metrics. The characterization highlighted the algorithm has good unpredictability characteristics with some opportunity for improvement to produce more randomness."