Analysis of Network Security Data Using Wavelet Transforms

Abstract

Data Analysis of Network Security is very important in intrusion detection and computer forensics. A lot of data mining methods to research it have been found, such as content-based queries and similarity searches to manage and use such data. Fast and accurate retrievals for content-based queries are crucial for such numerous data streams to be useful. In this paper, we apply wavelet transforms into network security to analyze and mine time-serial data streams for the detection of anomalous network security events. We first proposes a wavelet based data analysis framework for network security traffic, and signalize the data stream of network security (DSNS), then after de-noise of DSNS, we use wavelet based transforms to analyze the DSNS and get anomalous events for intrusion detection in computer network security. Experimental results show that, by using wavelet transform, we can decrease the noise signal and keep the useful signals of network security data streams to retrieve anomalous events effectively.