Reinforcement Learning-Controlled Mitigation of Volumetric DDoS Attacks
| dc.contributor.author | Heseding, Hauke | |
| dc.contributor.editor | Christian Wressnegger, Delphine Reinhardt | |
| dc.date.accessioned | 2023-01-24T11:17:51Z | |
| dc.date.available | 2023-01-24T11:17:51Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | This 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. | en |
| dc.identifier.doi | 10.18420/sicherheit2022_20 | |
| dc.identifier.isbn | 978-3-88579-717-3 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/40142 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik, Bonn | |
| dc.relation.ispartof | GI SICHERHEIT 2022 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-323 | |
| dc.subject | Distributed denial of service | |
| dc.subject | software defined networks | |
| dc.subject | hierarchical heavy hitters | |
| dc.subject | reinforcement learning | |
| dc.title | Reinforcement Learning-Controlled Mitigation of Volumetric DDoS Attacks | en |
| gi.citation.endPage | 242 | |
| gi.citation.startPage | 237 | |
| gi.conference.date | 5.-8. April 2022 | |
| gi.conference.location | Karlsruhe | |
| gi.conference.sessiontitle | Doktorand·innenforum |
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