S3 Lab - Software & Systems Security Laboratory
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CLUE

The CLUE project develops an infrastructure to detect and diagnose system anomalies in enterprise systems. These anomalies include stealthy malware and other types of hidden system anomalies. CLUE provides a diverse set of tools to find and understand such anomalies with minimal disruption to the target system.

people

publications

2020
Detecting Malware Injection with Program-DNS Behavior
Yixin Sun, Kangkook Jee, Suphannee Sivakorn, Zhichun Li, Cristian Lumezanu, Lauri Korts-Pàˆrn, Zhenyu Wu, John Junghwan Rhee, Chung Hwan Kim, Mung Chiang, and Prateek Mittal
In EuroS&P 2020 [ pdf :: bibtex ]
2019
Progressive Processing of System Behavioral Query
Jiaping Gui, Xusheng Xiao, Ding Li, Chung Hwan Kim, and Haifeng Chen
In ACSAC 2019 [ pdf :: slides :: bibtex ]
2018
SAQL: A Stream-based Query System for Real-Time Abnormal System Behavior Detection
Peng Gao, Xusheng Xiao, Ding Li, Zhichun Li, Kangkook Jee, Zhenyu Wu, Chung Hwan Kim, Sanjeev R. Kulkarni, and Prateek Mittal
In Security 2018 (award paper) [ pdf :: slides :: bibtex ]
2016
2015
Accurate, Low Cost and Instrumentation-Free Security Audit Logging for Windows
Shiqing Ma, Kyu Hyung Lee, Chung Hwan Kim, John Junghwan Rhee, Xiangyu Zhang, and Dongyan Xu
In ACSAC 2015 [ pdf :: slides :: bibtex ]
2014
IntroPerf: Transparent Context-Sensitive Multi-Layer Performance Inference using System Stack Traces
Chung Hwan Kim, John Junghwan Rhee, Hui Zhang, Nipun Arora, Guofei Jiang, Xiangyu Zhang, and Dongyan Xu
In SIGMETRICS 2014 [ pdf :: slides :: bibtex ]