Dependence-Preserving Data Compaction for Scalable Forensic Analysis

Authors: 

Md Nahid Hossain, Junao Wang, R. Sekar, and Scott D. Stoller, Stony Brook University

Abstract: 

Large organizations are increasingly targeted in long-running attack campaigns lasting months or years. When a break-in is eventually discovered, forensic analysis begins. System audit logs provide crucial information that underpins such analysis. Unfortunately, audit data collected over months or years can grow to enormous sizes. Large data size is not only a storage concern: forensic analysis tasks can become very slow when they must sift through billions of records. In this paper, we first present two powerful event reduction techniques that reduce the number of records by a factor of 4.6 to 19 in our experiments. An important benefit of our techniques is that they provably preserve the accuracy of forensic analysis tasks such as backtracking and impact analysis. While providing this guarantee, our techniques reduce on-disk file sizes by an average of 35× across our data sets. On average, our in-memory dependence graph uses just 5 bytes per event in the original data. Our system is able to consume and analyze nearly a million events per second.

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BibTeX
@inproceedings {217579,
author = {Md Nahid Hossain and Junao Wang and R. Sekar and Scott D. Stoller},
title = {Dependence-Preserving Data Compaction for Scalable Forensic Analysis},
booktitle = {27th {USENIX} Security Symposium ({USENIX} Security 18)},
year = {2018},
isbn = {978-1-931971-46-1},
address = {Baltimore, MD},
pages = {1723--1740},
url = {https://www.usenix.org/conference/usenixsecurity18/presentation/hossain},
publisher = {{USENIX} Association},
}