Biblio

Flow correlation can identify attackers who use anonymous networks or stepping stones. The current flow correlation scheme based on watermark can effectively trace the network traffic. But it is difficult to balance robustness and invisibility. This paper presents an innovative flow correlation scheme that guarantees invisibility. First, the scheme generates a two-dimensional feature matrix by segmenting the network flow. Then, features of frequency and time are extracted from the matrix and mapped into perceptual hash sequences. Finally, by comparing the hash sequence similarity to correlate the network flow, the scheme reduces the complexity of the correlation while ensuring the accuracy of the flow correlation. Experimental results show that our scheme is robust to jitter, packet insertion and loss.

In order to solve the problems of the existing speech content authentication algorithm, such as single format, ununiversal algorithm, low security, low accuracy of tamper detection and location in small-scale, a multi-format speech perception hashing based on time-frequency parameter fusion of energy zero ratio and frequency band bariance is proposed. Firstly, the algorithm preprocesses the processed speech signal and calculates the short-time logarithmic energy, zero-crossing rate and frequency band variance of each speech fragment. Then calculate the energy to zero ratio of each frame, perform time- frequency parameter fusion on time-frequency features by mean filtering, and the time-frequency parameters are constructed by difference hashing method. Finally, the hash sequence is scrambled with equal length by logistic chaotic map, so as to improve the security of the hash sequence in the transmission process. Experiments show that the proposed algorithm is robustness, discrimination and key dependent.