The static nature of current computing systems has made them easy to attack and harder to defend. Adversaries have an asymmetric advantage in that they have the time to study a system, identify its vulnerabilities, and choose the time and place of attack to gain the maximum benefit. The idea of moving-target defense (MTD) is to impose the same asymmetric disadvantage on attackers by making systems dynamic and therefore harder to explore and predict. With a constantly changing system and its ever adapting attack surface, attackers will have to deal with a great deal of uncertainty just like defenders do today. The ultimate goal of MTD is to increase the attackers’ workload so as to level the cybersecurity playing field for both defenders and attackers - hopefully even tilting it in favor of the defender.
We welcome all works that fall under the broad scope of moving target defense, including research that shows negative results.
System randomization
Artificial diversity
Cyber maneuver
Bio-inspired defenses
Dynamic network configuration
Moving target in the cloud
System diversification techniques
Dynamic compilation techniques
Adaptive defenses
MTD quantification methods and models
Large-scale MTD (using multiple techniques)
Moving target in software coding, application APIs virtualization
Autonomous technologies for MTD
Theoretic study on modeling trade-offs of using MTD approaches
Human, social, and psychology aspects of MTD
Other related areas
10月24日
2016
10月28日
2016
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