Visible to the public A Network Covert Timing Channel Detection Method Based on Chaos Theory and Threshold Secret Sharing

TitleA Network Covert Timing Channel Detection Method Based on Chaos Theory and Threshold Secret Sharing
Publication TypeConference Paper
Year of Publication2020
AuthorsXie, J., Chen, Y., Wang, L., Wang, Z.
Conference Name2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)
Date PublishedJune 2020
PublisherIEEE
ISBN Number978-1-7281-4390-3
Keywordschannel identifier, chaos, chaos theory, compositionality, covert channels, high-dimensional phase space, inter-packet delay, NCTC detection method, network covert timing channel, network covert timing channel detection method, network traffic, one-dimensional time series, pubcrawl, resilience, Resiliency, Robustness, Scalability, secret reconstruction strategy, security concern, security of data, stable channel traits, telecommunication channels, telecommunication traffic, Threshold secret sharing, time series, traditional security policies, unique channel traits
Abstract

Network covert timing channel(NCTC) is a process of transmitting hidden information by means of inter-packet delay (IPD) of legitimate network traffic. Their ability to evade traditional security policies makes NCTCs a grave security concern. However, a robust method that can be used to detect a large number of NCTCs is missing. In this paper, a NCTC detection method based on chaos theory and threshold secret sharing is proposed. Our method uses chaos theory to reconstruct a high-dimensional phase space from one-dimensional time series and extract the unique and stable channel traits. Then, a channel identifier is constructed using the secret reconstruction strategy from threshold secret sharing to realize the mapping of the channel features to channel identifiers. Experimental results show that the approach can detect varieties of NCTCs with a guaranteed true positive rate and greatly improve the versatility and robustness.

URLhttps://ieeexplore.ieee.org/document/9085024
DOI10.1109/ITNEC48623.2020.9085024
Citation Keyxie_network_2020