HeteMSD: A Big Data Analytics Framework for Targeted Cyber-Attacks Detection Using Heterogeneous Multisource Data

Abstract

In the current enterprise network environment, multistep targeted cyber-attacks with concealment and advanced characteristics have become the main threat. Multisource security data are the prerequisite of targeted cyber-attacks detection. However, these data have characters of heterogeneity and semantic diversity, and existing attack detection methods do not take comprehensive data sources into account. Identifying and predicting attack intention from heterogeneous noisy data can be meaningful work. In this paper, we first review different data fusion mechanisms of correlating heterogeneous multisource data. On this basis, we propose a big data analytics framework for targeted cyber-attacks detection and give the basic idea of correlation analysis. Our approach will offer the ability to correlate multisource heterogeneous security data and analyze attack intention effectively.