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Moving Target Defense (MTD) has emerged as a newcomer into the asymmetricfield of attack and defense, and shuffling-based MTD has been regarded as oneof the most effective ways to mitigate DDoS attacks. However, previous workdoes not acknowledge that frequent shuffles would significantly intensify theoverhead. MTD requires a quantitative measure to compare the cost andeffectiveness of available adaptations and explore the best trade-off betweenthem. In this paper, therefore, we propose a new cost-effective shufflingmethod against DDoS attacks using MTD. By exploiting Multi-Objective MarkovDecision Processes to model the interaction between the attacker and thedefender, and designing a cost-effective shuffling algorithm, we study the besttrade-off between the effectiveness and cost of shuffling in a given shufflingscenario. Finally, simulation and experimentation on an experimental softwaredefined network (SDN) indicate that our approach imposes an acceptableshuffling overload and is effective in mitigating DDoS attacks.

A Cost-effective Shuffling Method against DDoS Attacks using Moving Target Defense