Visualization of big data security： a case study on the KDD99 cup data set

作     者：Zichan Ruan Yuantian Miao Lei Pan Nicholas Patterson Jun Zhang 

作者机构：School of Information Technology Deakin University Geelong VIC 3220 Australia 

出 版 物：《Digital Communications and Networks》 (数字通信与网络（英文版）)

年 卷 期：2017年第3卷第4期

页      面：250-259页

核心收录：

学科分类：080904[工学-电磁场与微波技术] 0810[工学-信息与通信工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术] 081001[工学-通信与信息系统] 

主　　题：Big data visualization Sampling method MDS PCA 

摘      要：Cyber security has been thrust into the limelight in the modern technological era because of an array of attacks often bypassing tmtrained intrusion detection systems （IDSs）. Therefore, greater attention has been directed on being able deciphering better methods for identifying attack types to train IDSs more effectively. Keycyber-attack insights exist in big data; however, an efficient approach is required to determine strong attack types to train IDSs to become more effective in key areas. Despite the rising growth in IDS research, there is a lack of studies involving big data visualization, which is key. The KDD99 data set has served as a strong benchmark since 1999; therefore, we utilized this data set in our experiment. In this study, we utilized hash algorithm, a weight table, and sampling method to deal with the inherent problems caused by analyzing big data; volume, variety, and velocity. By utilizing a visualization algorithm, we were able to gain insights into the KDD99 data set with a clear iden- tification of ＂normal＂ clusters and described distinct clusters of effective attacks.