By Topic

An Approach to Privacy-Preserving Alert Correlation and Analysis

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Jin Ma ; Electron. Inf. & Electr. Eng. Sch., Shanghai Jiao Tong Univ., Shanghai, China ; Xiu-zhen Chen ; Jian-Hua Li

Privacy issues are concerned when data holders share their detected security data for correlation and analysis purpose. This paper proposes an approach to correlate and analyze intrusion alerts, while preserve privacy for alert holders. The raw intrusion alerts are protected by improved k-anonymity model, which preserves the alert regulation inside disturbed data records. With this privacy preserving technique, combing the typical FP-tree association rules mining algorithm, the approach provides the capacity of well balancing the alert correlation and the privacy preservation. Experimental results show that this approach works comparatively efficient and reaches a well balance between the alerts correlation and the privacy issues.

Published in:

Services Computing Conference (APSCC), 2010 IEEE Asia-Pacific

Date of Conference:

6-10 Dec. 2010