New Directions in Automated Traffic Analysis

Jordan Holland, Paul Schmitt, Nicholas G. Feamster, Prateek Mittal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Machine learning is leveraged for many network traffic analysis tasks in security, from application identification to intrusion detection. Yet, the aspects of the machine learning pipeline that ultimately determine the performance of the model - -feature selection and representation, model selection, and parameter tuning - -remain manual and painstaking. This paper presents a method to automate many aspects of traffic analysis, making it easier to apply machine learning techniques to a wider variety of traffic analysis tasks. We introduce nPrint, a tool that generates a unified packet representation that is amenable for representation learning and model training. We integrate nPrint with automated machine learning (AutoML), resulting in nPrintML, a public system that largely eliminates feature extraction and model tuning for a wide variety of traffic analysis tasks. We have evaluated nPrintML on eight separate traffic analysis tasks and released nPrint, nPrintML and the corresponding datasets from our evaluation to enable future work to extend these methods.

Original languageEnglish (US)
Title of host publicationCCS 2021 - Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security
PublisherAssociation for Computing Machinery
Pages3366-3383
Number of pages18
ISBN (Electronic)9781450384544
DOIs
StatePublished - Nov 12 2021
Event27th ACM Annual Conference on Computer and Communication Security, CCS 2021 - Virtual, Online, Korea, Republic of
Duration: Nov 15 2021Nov 19 2021

Publication series

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

Conference

Conference27th ACM Annual Conference on Computer and Communication Security, CCS 2021
Country/TerritoryKorea, Republic of
CityVirtual, Online
Period11/15/2111/19/21

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Networks and Communications

Keywords

  • automated traffic analysis
  • machine learning on network traffic
  • network traffic analysis

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