Gray-Box Extraction of Execution Graphs for Anomaly Detection
Publication Type
Conference Proceeding Article
Publication Date
10-2004
Abstract
Many host-based anomaly detection systems monitor a process by observing the system calls it makes, and comparing these calls to a model of behavior for the program that the process should be executing. In this paper we introduce a new model of system call behavior, called an execution graph. The execution graph is the first such model that both requires no static analysis of the program source or binary, and conforms to the control flow graph of the program. When used as the model in an anomaly detection system monitoring system calls, it offers two strong properties: (i) it accepts only system call sequences that are consistent with the control flow graph of the program; (ii) it is maximal given a set of training data, meaning that any extensions to the execution graph could permit some intrusions to go undetected. In this paper, we formalize and prove these claims. We additionally evaluate the performance of our anomaly detection technique.
Discipline
Information Security
Research Areas
Information Security and Trust
Publication
CCS 2004: Proceedings of the 11th ACM Conference on Computer and Communications Security, October 25-29, 2004, Washington, DC
First Page
318
Last Page
329
ISBN
9781581139617
Identifier
10.1145/1030083.1030126
Publisher
ACM
City or Country
Washington, DC, USA
Citation
GAO, Debin; Reiter, Michael K.; and SONG, Dawn.
Gray-Box Extraction of Execution Graphs for Anomaly Detection. (2004). CCS 2004: Proceedings of the 11th ACM Conference on Computer and Communications Security, October 25-29, 2004, Washington, DC. 318-329.
Available at: https://ink.library.smu.edu.sg/sis_research/1242
Additional URL
http://dx.doi.org/10.1145/1030083.1030126