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The Center for Education and Research in Information Assurance and Security (CERIAS)

The Center for Education and Research in
Information Assurance and Security (CERIAS)

Shiqing Ma - Purdue University

Students: Fall 2021, unless noted otherwise, sessions will be virtual on Zoom.

Kernel-Supported Cost-Effective Audit Logging for Causality Tracking

Sep 26, 2018

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Abstract

The Linux Audit system is widely used as a causality tracking system in real-world deployments for problem diagnosis and forensic analysis. However, it has poor performance. We perform a comprehensive analysis on the Linux Audit system and find that it suffers from high runtime and storage overheads due to the large volume of redundant events. To address these shortcomings, we propose an in-kernel cache-based online log-reduction system to enable high-performance audit logging. It features a multi-layer caching scheme distributed in various kernel data structures, and uses the caches to detect and suppress redundant events. Our technique is designed to reduce the runtime overhead caused by transferring, processing, and writing logs, as well as the space overhead caused by storing them on disk. Compared to existing log reduction techniques that first generate the huge raw logs before reduction, our technique avoids generating redundant events at the first place. Our experimental results of the prototype KCAL (Kernel-supported Cost-effective Audit Logging) on one-month real-world workloads show that KCAL can reduce the runtime overhead from 40+% to 15-%, and reduce space consumption by 90% on average. KCAL achieves such a large reduction with 4% CPU consumption on average, whereas a state-of-the-art user space log-reduction technique has to occupy a processor with 95+% CPU consumption all the time.

About the Speaker

Shiqing Ma is a Ph.D. candidate from the Department of Computer Science at Purdue University, advised by Dr. Xiangyu Zhang and Dr. Dongyan Xu. He received his B.E. from School of Software Engineering, Shanghai Jiao Tong University (SJTU) in 2013. His research focuses on system/software security, software engineering and machine learning. He is a recipient of the Bilsland Dissertation Fellowship and two Distinguished Paper Awards from NDSS 2016 and USENIX Security 2017.


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