Peng Tao; Chen Xiaosu; Liu Huiyu; Chen Kai

Density-Based LLE Algorithm for Network Forensics Data

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Author(s)

Peng Tao ¹ Chen Xiaosu ^1,* Liu Huiyu ¹ Chen Kai ¹

1. School of Computer Science and Technology of Huazhong University of Science and Technology

* Corresponding author.

DOI: https://doi.org/10.5815/ijmecs.2011.01.08

Received: 16 Nov. 2010 / Revised: 20 Dec. 2010 / Accepted: 14 Jan. 2011 / Published: 8 Feb. 2011

Index Terms

Data Reduction, Network Forensics, Manifold Learning, LLE

Abstract

In a network forensic system, there are huge amounts of data that should be processed, and the data contains redundant and noisy features causing slow training and testing processes, high resource consumption as well as poor detection rate. In this paper, a schema is proposed to reduce the data of the forensics using manifold learning. Manifold learning is a popular recent approach to nonlinear dimensionality reduction. Algorithms for this task are based on the idea that the dimensionality of many data sets is only artificially high. In this paper, we reduce the forensic data with manifold learning, and test the result of the reduced data.

Cite This Paper

Peng Tao, Chen Xiaosu, Liu Huiyu, Chen Kai, "Density-Based LLE Algorithm for Network Forensics Data", International Journal of Modern Education and Computer Science(IJMECS), vol.3, no.1, pp.52-59, 2011. DOI:10.5815/ijmecs.2011.01.08

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International Journal of Modern Education and Computer Science (IJMECS)