Talks and Poster Presentations (with Proceedings-Entry):
F. Iglesias Vazquez, V. Bernhardt, R. Annessi, T. Zseby:
"Decision Tree Rule Induction for Detecting Covert Timing Channels in TCP/IP Traffic";
Talk: CD-MAKE 2017: IFIP Cross Domain Conference for Machine Learning & Knowledge Extraction,
Reggio di Calabria, Italy;
- 09-01-2017; in: "Proceedings of the Machine Learning and Knowledge Extraction: First IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference",
The detection of covert channels in communication networks is a current security challenge. By clandestinely transferring information, covert channels are able to circumvent security barriers, compromise systems, and facilitate data leakage. A set of statistical methods called DAT (Descriptive Analytics of Traffic) has been previously proposed as a general approach for detecting covert channels. In this paper, we implement and evaluate DAT detectors for the specific case of covert timing channels. Additionally, we propose machine learning models to induce classification rules and enable the fine parameterization of DAT detectors. A testbed has been created to reproduce main timing techniques published in the literature; consequently, the testbed allows the evaluation of covert channel detection techniques. We specifically applied Decision Trees to infer DAT-rules, achieving high accuracy and detection rates. This paper is a step forward for the actual implementation of effective covert channel detection plugins in modern network security devices.
covert channels, decision trees, forensic analysis, machine learning, network communications, statistics
Electronic version of the publication:
Created from the Publication Database of the Vienna University of Technology.