Supervised learning framework for covert channel detection in LTE-A

Supervised learning framework for covert channel detection in LTE-A

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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.

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