PS-IPS: Deploying Intrusion Prevention System with machine learning on programmable switch

Alan Y.P. Lee, Michael I.C. Wang, Chi Hsiang Hung*, Charles H.P. Wen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Intrusion prevention is significant to avoid device damage and financial losses. Researchers have proposed various Intrusion Prevention Systems (IPS) to prevent malware, including traditional and SDN-based IPS. However, existing IPSs suffer from low throughput problems caused by detection and rule-installation delays. Here, we propose a programmable switch-base IPS (named PS-IPS), which utilizes the switch CPU and pipeline to detect malware. PS-IPS consists of four main components: (1) parser, (2) flow filter, (3) recirculation director, and (4) malware detector. According to the experiment, PS-IPS achieves a 183X throughput than the SDN-based IPS. The response time of PS-IPS is also reduced by 99.99%, showing that PS-IPS effectively prevents malware with a single programmable switch.

Original languageEnglish
Pages (from-to)333-342
Number of pages10
JournalFuture Generation Computer Systems
Volume152
DOIs
StatePublished - Mar 2024

Keywords

  • Intrusion prevention system
  • Machine learning
  • Network security
  • P4
  • Programmable switch
  • Software defined networks

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