Data Reduction for Network Forensics Using Manifold Learning

Abstract

In network forensic system, there are huge amount of data should be processed, and the data contains redundant and noisy features causing slow training and testing process, 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.