RT Journal Article
A1 Guangliang Xu
A1 Wei Yang
A1 Liusheng Huang

PB iet
T1 Supervised learning framework for covert channel detection in LTE-A
JN IET Information Security
VO 12
IS 6
SP 534
OP 542
AB Covert channels transmit secret information by using the existing resources which were not designed for communication. As a major approach to information leakage, covert channels are rapidly gaining popularity with the exponentially growth of cloud and network resources. Long Term Evolution Advance (LTE-A) has dominated the mobile telecommunication networks, which brings an elevation of the risk of covert channels. In this study, the authors propose a supervised learning scheme based on support vector machine (SVM) for the covert channel detection in LTE-A. Based on the fact that the covert channel using the header fields of LTE-A protocol would change the regularity, goodness of fit or correlation of the data traffic, they present behaviour characteristics statistics index (CSI) in the LTE-A protocol to evaluate the changes. According to CSI, they extract the classification feature vectors from the data traffic stream, based on which an SVM classifier used for classifying the channel as covert or overt is trained for testing on the channel under investigation. Experiment results show that the authors' proposed detection scheme is high-efficiency in terms of detection accuracy, sensitivity and specificity, which has great potential to serve as a new idea for the detection of covert channel in LTE-A.
K1 SVM classifier
K1 LTE-A protocol
K1 covert channel detection
K1 Long Term Evolution Advance
K1 mobile telecommunication networks
K1 supervised learning framework
K1 support vector machine
DO https://doi.org/10.1049/iet-ifs.2017.0394
UL https://digital-library.theiet.org/;jsessionid=3arpin5t3cohi.x-iet-live-01content/journals/10.1049/iet-ifs.2017.0394
LA English
SN 1751-8709
YR 2018
OL EN