Heseding, HaukeChristian Wressnegger, Delphine Reinhardt2023-01-242023-01-242022978-3-88579-717-3https://dl.gi.de/handle/20.500.12116/40142This work introduces a novel approach to combine hierarchical heavy hitter algorithms with reinforcement learning to mitigate evolving volumetric distributed denial of service attacks. The goal is to alleviate the strain on the network infrastructure through early ingress filtering based on compact filter rule sets that are evaluated by fast ternary content-addressable memory. The reinforcement learning agents task is to maintain effectiveness of established filter rules even in dynamic traffic scenarios while preserving limited memory resources. Preliminary results based on synthesized traffic scenarios modelling dynamic attack patterns indicate the feasibility of our approach.enDistributed denial of servicesoftware defined networkshierarchical heavy hittersreinforcement learning10.18420/sicherheit2022_201617-5468