A privacy-preserving framework for collaborative intrusion detection networks through fog computing

Yu Wang, Lin Xie, Wenjuan Li, Weizhi Meng, Jin Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

13 Citations (Scopus)

Abstract

Nowadays, cyber threats (e.g., intrusions) are distributed across various networks with the dispersed networking resources. Intrusion detection systems (IDSs) have already become an essential solution to defend against a large amount of attacks. With the development of cloud computing, a modern IDS is able to implement more complicated detection algorithms by offloading the expensive operations such as the process of signature matching to the cloud (i.e., utilizing computing resources from the cloud). However, during the detection process, no party wants to disclose their own data especially sensitive information to others for privacy concerns, even to the cloud side. For this sake, privacy-preserving technology has been applied to IDSs, while it still lacks of proper solutions for a collaborative intrusion detection network (CIDN) due to geographical distribution. A CIDN enables a set of dispersed IDS nodes to exchange required information. With the advent of fog computing, in this paper, we propose a privacy-preserving framework for collaborative networks based on fog devices. Our study shows that the proposed framework can help reduce the workload on cloud’s side.

Original languageEnglish
Title of host publicationCyberspace Safety and Security - 9th International Symposium, CSS 2017, Proceedings
EditorsWei Wu, Aniello Castiglione, Sheng Wen
PublisherSpringer Verlag
Pages267-279
Number of pages13
ISBN (Print)9783319694702
DOIs
Publication statusPublished - 2017
Externally publishedYes
Event9th International Symposium on Cyberspace Safety and Security, CSS 2017 - Xi'an, China
Duration: 23 Oct 201725 Oct 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10581 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th International Symposium on Cyberspace Safety and Security, CSS 2017
Country/TerritoryChina
CityXi'an
Period23/10/1725/10/17

Keywords

  • Cloud environment
  • Collaborate network
  • Fog computing
  • Intrusion detection
  • Privacy preserving

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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