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/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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, Xi’an China, October 23–25, 2017, Proceedings
Volume10581
PublisherSpringer
Publication date2017
Edition1
Pages267-279
ISBN (Print)978-3-319-69470-2
ISBN (Electronic)978-3-319-69471-9
DOIs
Publication statusPublished - 2017
Event9th International Symposium on Cyberspace Safety and Security - Xi'an Jiyuan International Hotel , Xi'an , China
Duration: 23 Oct 201725 Oct 2017

Conference

Conference9th International Symposium on Cyberspace Safety and Security
Location Xi'an Jiyuan International Hotel
CountryChina
City Xi'an
Period23/10/201725/10/2017
SeriesLecture Notes in Computer Science
ISSN0302-9743

Keywords

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

Cite this

Wang, Y., Xie, L., Li, W., Meng, W., & Li, J. (2017). A Privacy-Preserving Framework for Collaborative Intrusion Detection Networks Through Fog Computing. In Cyberspace Safety and Security: 9th International Symposium, CSS 2017, Xi’an China, October 23–25, 2017, Proceedings (1 ed., Vol. 10581, pp. 267-279). Springer. Lecture Notes in Computer Science https://doi.org/10.1007/978-3-319-69471-9_20
Wang, Yu ; Xie, Lin ; Li, Wenjuan ; Meng, Weizhi ; Li, Jin. / A Privacy-Preserving Framework for Collaborative Intrusion Detection Networks Through Fog Computing. Cyberspace Safety and Security: 9th International Symposium, CSS 2017, Xi’an China, October 23–25, 2017, Proceedings. Vol. 10581 1. ed. Springer, 2017. pp. 267-279 (Lecture Notes in Computer Science).
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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.",
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Wang, Y, Xie, L, Li, W, Meng, W & Li, J 2017, A Privacy-Preserving Framework for Collaborative Intrusion Detection Networks Through Fog Computing. in Cyberspace Safety and Security: 9th International Symposium, CSS 2017, Xi’an China, October 23–25, 2017, Proceedings. 1 edn, vol. 10581, Springer, Lecture Notes in Computer Science, pp. 267-279, 9th International Symposium on Cyberspace Safety and Security, Xi'an , China, 23/10/2017. https://doi.org/10.1007/978-3-319-69471-9_20

A Privacy-Preserving Framework for Collaborative Intrusion Detection Networks Through Fog Computing. / Wang, Yu; Xie, Lin; Li, Wenjuan; Meng, Weizhi; Li, Jin.

Cyberspace Safety and Security: 9th International Symposium, CSS 2017, Xi’an China, October 23–25, 2017, Proceedings. Vol. 10581 1. ed. Springer, 2017. p. 267-279 (Lecture Notes in Computer Science).

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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AB - 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.

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Wang Y, Xie L, Li W, Meng W, Li J. A Privacy-Preserving Framework for Collaborative Intrusion Detection Networks Through Fog Computing. In Cyberspace Safety and Security: 9th International Symposium, CSS 2017, Xi’an China, October 23–25, 2017, Proceedings. 1 ed. Vol. 10581. Springer. 2017. p. 267-279. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-319-69471-9_20