A Scalable and Dynamic ACL System for In-Network Defense

Changhun Jung, Sian Kim, Rhongho Jang, David Mohaisen, Dae Hun Nyang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations


In-network/in-switch Access Control List (ACL) is an essential security component of modern networks. In high-speed networks, ACL rules are often placed in a switch's Ternary Content-Addressable Memory (TCAM) for timely ACL match-action and management (e.g. insertion and deletion). However, TCAM-based ACL systems are encountering an scalability issue owing to increasing demand on AI-powered autonomous defenses that detect and block attacks online, which inevitably derives finer-grained ACL rules. Existing solutions minimize the TCAM usage by partially offloading ACL matching into larger Static Random-Access Memory (SRAM) or customized hardware. Nevertheless, current SRAM-based solutions induce high management costs, especially a high rule-deployment latency, which delays time-sensitive defense actions. Also, the customized hardware approaches have its own scalability issue. To support autonomous defenses at a scale, in this paper, we propose an in-switch ACL system called PortCatcher, which breaks the trade-off between scalability and rule management latency. System-wise, we detach layer-4 port matching from TCAM for improving its memory efficiency. Algorithm-wise, we introduce a novel port (range) rule representation concept, called linear range map (LRM), which enables port (range) matching in SRAM-based hash tables. LRM guarantees not only fast and scalable port matching but also low-latency ACL management for timely defenses. With real-world ACL datasets, we show that PortCatcher saves 74%-90% TCAM space compared to state-of-the-art approaches by adding small overhead to SRAM (0.49 SRAM entry per ACL rule). Also, we deploy PortCatcher on a programmable switch to demonstrate that PortCatcher can serve 5-tuple rule matching at a line rate, where port rules are completely matched in SRAM. With a use case study, namely autonomous attack mitigation, we show that PortCatcher has a negligible rule management latency to block attack flows (i.e. 94.42% of rules deployed within 10 ms).

Original languageEnglish
Title of host publicationCCS 2022 - Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security
PublisherAssociation for Computing Machinery
Number of pages15
ISBN (Electronic)9781450394505
StatePublished - 7 Nov 2022
Event28th ACM SIGSAC Conference on Computer and Communications Security, CCS 2022 - Los Angeles, United States
Duration: 7 Nov 202211 Nov 2022

Publication series

NameProceedings of the ACM Conference on Computer and Communications Security
ISSN (Print)1543-7221


Conference28th ACM SIGSAC Conference on Computer and Communications Security, CCS 2022
Country/TerritoryUnited States
CityLos Angeles

Bibliographical note

Publisher Copyright:
© 2022 ACM.


  • dynamic management
  • in-network acl
  • low-latency defense
  • scalable port (range) matching


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