By Topic

A Grid-Based Clustering Algorithm for Network Anomaly Detection

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)
Xiaotao Wei ; Beijing Jiaotong Univ., Beijing ; Houkuan Huang ; ShengFeng Tian

In this paper, we proposed a two phase grid-based clustering algorithm to partition network traffic data. The first phase is a grid-based preclustering stage. The domain space is divided into un-overlapping d-dimensional cells. The second phase is a novel partition-based clustering procedure we referred to as k-hypercells. It directly takes the populated cells created by the first phase as the source data for clustering. The algorithm can automatically decide the number of clusters and is designed specially for handling the high-dimensional categorical data records. The experimental result shows that our algorithm is efficient and effective for compressing and partitioning high-dimensional large data spaces.

Published in:

Data, Privacy, and E-Commerce, 2007. ISDPE 2007. The First International Symposium on

Date of Conference:

1-3 Nov. 2007