NodeMerge: Template Based Efficient Data Reduction For Big-Data Causality Analysis

/in /by

Publication Date: 10/19/2018

Event: Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security (ACM CCS 2018)

Reference: pp. 1324-1337, 2018

Authors: Yutao Tang, College of William and Mary; Ding Li, NEC Laboratories America, Inc.; Zhichun Li, NEC Laboratories America, Inc.; Mu Zhang, Cornell University; Kangkook Jee, NEC Laboratories America, Inc.; Junghwan Rhee, NEC Laboratories America, Inc.; Zhenyu Wu, NEC Laboratories America, Inc.; Xusheng Xiao, Case Western Reserve University; Fengyuan Xu, Nanjing University; Qun Li, College of William and Mary

Abstract: Today’s enterprises are exposed to sophisticated attacks, such as Advanced Persistent Threats~(APT) attacks, which usually consist of stealthy multiple steps. To counter these attacks, enterprises often rely on causality analysis on the system activity data collected from a ubiquitous system monitoring to discover the initial penetration point, and from there identify previously unknown attack steps. However, one major challenge for causality analysis is that the ubiquitous system monitoring generates a colossal amount of data and hosting such a huge amount of data is prohibitively expensive. Thus, there is a strong demand for techniques that reduce the storage of data for causality analysis and yet preserve the quality of the causality analysis. To address this problem, in this paper, we propose NodeMerge, a template based data reduction system for online system event storage. Specifically, our approach can directly work on the stream of system dependency data and achieve data reduction on the read-only file events based on their access patterns. It can either reduce the storage cost or improve the performance of causality analysis under the same budget. Only with a reasonable amount of resource for online data reduction, it nearly completely preserves the accuracy for causality analysis. The reduced form of data can be used directly with little overhead. To evaluate our approach, we conducted a set of comprehensive evaluations, which show that for different categories of workloads, our system can reduce the storage capacity of raw system dependency data by as high as 75.7 times, and the storage capacity of the state-of-the-art approach by as high as 32.6 times. Furthermore, the results also demonstrate that our approach keeps all the causality analysis information and has a reasonably small overhead in memory and hard disk.

Publication Link: https://dl.acm.org/doi/10.1145/3243734.3243763