Towards Ground Truthing Observations in Gray-Box Anomaly Detection

Anomaly detection has been attracting interests from researchers due to its advantage of being able to detect zero-day exploits. A gray-box anomaly detector first observes benign executions of a computer program and then extracts reliable rules that govern the normal execution of the program. Howeve...

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Main Authors: MING, Jiang, ZHANG, Haibin, GAO, Debin
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Language:English
Published: Institutional Knowledge at Singapore Management University 2011
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Online Access:https://ink.library.smu.edu.sg/sis_research/2006
https://ink.library.smu.edu.sg/context/sis_research/article/3005/viewcontent/nss11.pdf
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spelling sg-smu-ink.sis_research-30052020-01-26T08:10:23Z Towards Ground Truthing Observations in Gray-Box Anomaly Detection MING, Jiang ZHANG, Haibin GAO, Debin Anomaly detection has been attracting interests from researchers due to its advantage of being able to detect zero-day exploits. A gray-box anomaly detector first observes benign executions of a computer program and then extracts reliable rules that govern the normal execution of the program. However, such observations from benign executions are not necessarily true evidences supporting the rules learned. For example, the observation that a file descriptor being equal to a socket descriptor should not be considered supporting a rule governing the two values to be the same. Ground truthing such observations is a difficult problem since it is not practical to analyze the semantics of every instruction in every program to be protected. In this paper, we propose using taint analysis to automatically help the ground truthing. Intuitively, the same taint source of two values provides ground truth of the data dependence. We implement a host-based anomaly detector with our proposed taint tracking and evaluate the accuracy of rules learned. Results show that we not only manage to filter out incorrect rules that would otherwise be learned (with high support and confidence), but manage recover good rules that are previously believed to be unreliable. We also present overheads of our system and time needed for training. 2011-09-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/2006 info:doi/10.1109/ICNSS.2011.6059956 https://ink.library.smu.edu.sg/context/sis_research/article/3005/viewcontent/nss11.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University anomaly detection taint analysis system call monitor ground truthing Information Security
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic anomaly detection
taint analysis
system call monitor
ground truthing
Information Security
spellingShingle anomaly detection
taint analysis
system call monitor
ground truthing
Information Security
MING, Jiang
ZHANG, Haibin
GAO, Debin
Towards Ground Truthing Observations in Gray-Box Anomaly Detection
description Anomaly detection has been attracting interests from researchers due to its advantage of being able to detect zero-day exploits. A gray-box anomaly detector first observes benign executions of a computer program and then extracts reliable rules that govern the normal execution of the program. However, such observations from benign executions are not necessarily true evidences supporting the rules learned. For example, the observation that a file descriptor being equal to a socket descriptor should not be considered supporting a rule governing the two values to be the same. Ground truthing such observations is a difficult problem since it is not practical to analyze the semantics of every instruction in every program to be protected. In this paper, we propose using taint analysis to automatically help the ground truthing. Intuitively, the same taint source of two values provides ground truth of the data dependence. We implement a host-based anomaly detector with our proposed taint tracking and evaluate the accuracy of rules learned. Results show that we not only manage to filter out incorrect rules that would otherwise be learned (with high support and confidence), but manage recover good rules that are previously believed to be unreliable. We also present overheads of our system and time needed for training.
format text
author MING, Jiang
ZHANG, Haibin
GAO, Debin
author_facet MING, Jiang
ZHANG, Haibin
GAO, Debin
author_sort MING, Jiang
title Towards Ground Truthing Observations in Gray-Box Anomaly Detection
title_short Towards Ground Truthing Observations in Gray-Box Anomaly Detection
title_full Towards Ground Truthing Observations in Gray-Box Anomaly Detection
title_fullStr Towards Ground Truthing Observations in Gray-Box Anomaly Detection
title_full_unstemmed Towards Ground Truthing Observations in Gray-Box Anomaly Detection
title_sort towards ground truthing observations in gray-box anomaly detection
publisher Institutional Knowledge at Singapore Management University
publishDate 2011
url https://ink.library.smu.edu.sg/sis_research/2006
https://ink.library.smu.edu.sg/context/sis_research/article/3005/viewcontent/nss11.pdf
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