Network Storage Covert Channel Detection Based on Data Joint Analysis

Aiming at the problem that the existing network storage covert channel detection algorithm can not take into account both the detection rate and the computational complexity, a network storage covert channel detection method based on data joint analysis is proposed. This method studies the information hiding mechanism of the network storage covert channel according to related documents. Based on this, the regularity characteristics of the packets in each field of the network data packet and the correlation characteristics between the packets are analyzed. The above characteristics are further transformed into eigenvector matrices through kernel density estimation, variation coefficient, fragility entropy, and autocorrelation coefficient. And SVM classifier is trained using eigenvector matrices. The experimental test shows that this method has a high detection rate and its computational complexity is small.