Paper Info
Title
Privacy preserving data mining of sequential patterns for network traffic data
Abstract
As the total amount of traffic data in networks has been growing at an alarming rate, there is currently a substantial body of research that attempts to mine traffic data with the purpose of obtaining useful information. For instance, there are some investigations into the detection of Internet worms and intrusions by discovering abnormal traffic patterns. However, since network traffic data contain information about the Internet usage patterns of users, network users' privacy may be compromised during the mining process. In this paper, we propose an efficient and practical method that preserves privacy during sequential pattern mining on network traffic data. In order to discover frequent sequential patterns without violating privacy, our method uses the N-repository server model, which operates as a single mining server and the retention replacement technique, which changes the answer to a query probabilistically. In addition, our method accelerates the overall mining process by maintaining the meta tables in each site so as to determine quickly whether candidate patterns have ever occurred in the site or not. Extensive experiments with real-world network traffic data revealed the correctness and the efficiency of the proposed method.
Year
DOI
Venue
2008
10.1016/j.ins.2007.08.022
Information Sciences
Keywords
Field
DocType
data mining,mining process,abnormal traffic pattern,practical method,sequential pattern mining,traffic data,real-world network traffic data,network user,overall mining process,network traffic data,privacy
Data mining,Data stream mining,Computer science,Correctness,Sequential Pattern Mining,Privacy software,Database,The Internet
Journal
Volume
Issue
ISSN
178
3
0302-9743
Citations 
PageRank 
References 
20
0.74
24
Authors
4
Name
Order
Citations
PageRank
Seung-Woo Kim123115.16
Sanghyun Park272980.64
Jung-Im Won38610.56
Sang-wook Kim412248.24