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Advanced cyber attacks consist of multiple stages aimed at being stealthy and elusive. Such attack patterns leave their footprints spatio-temporally dispersed across many different logs in victim machines. However, existing log-mining intrusion analysis systems typically target only a single type of log to discover evidence of an attack and therefore fail to exploit fundamental inter-log connections. The output of such single-log analysis can hardly reveal the complete attack story for complex, multi-stage attacks. Additionally, some existing approaches require heavyweight system instrumentation, which makes them impractical to deploy in real production environments. To address these problems, we present HERCULE, an automated multi-stage log-based intrusion analysis system. Inspired by graph analytics research in social network analysis, we model multi-stage intrusion analysis as a community discovery problem. HERCULE builds multi-dimensional weighted graphs by correlating log entries across multiple lightweight logs that are readily available on commodity systems. From these, HERCULE discovers any ``attack communities'' embedded within the graphs. Our evaluation with 15 well known APT attack families demonstrates that HERCULE can reconstruct attack behaviors from a spectrum of cyber attacks that involve multiple stages with high accuracy and low false positive rates.
Author(s):
Kexin Pei
Columbia University
United States
Zhongshu Gu
IBM T.J. Watson Research Center
United States
Brendan Saltaformaggio
Purdue University
United States
Shiqing Ma
Purdue University
United States
Fei Wang
Purdue University
United States
Zhiwei Zhang
Purdue University
United States
Luo Si
Purdue University
United States
Xiangyu Zhang
Purdue University
United States
Dongyan Xu
Purdue University
United States