The Research On The Algorithms Of Information Hiding And Network Traffic Detection Based On Wavelet Analysis

Information hiding and network traffic detection are important and fundamental techniques of content security and operation security respectively in information security. Mainly based on wavelet and its generalizations-bandelet, coupled with empirical model decomposition and support vector machine, this thesis deals with new algorithms for information hiding of two-dimensional images and three-dimensional landform data, and detection of network traffic anomalies as well as identification of P2P. The main contributions of the thesis are as follows:The algorithms of information hiding for two-dimensional images via new types of wavelet-bandelet are proposed. The traditional watermark protects the digital image by modifying the image data to hide the information used to authenticate the image copyright. It is not applicable to the protection of images which are not permitted to be changed. Lossless watermarking of characteristic class is proposed. Wavelet transform is first performed for the original image, then transformed for the high and middle frequency part of the image. The geometric flow is traced by bandelet. Texture and edge as the feature parameters of the image are used to construct lossless watermarking of high frequency. For low frequency part of wavelet coefficients, a new watermarking scheme is then proposed by selecting optimal matrix norm. The method is used to protect the image from all-around attacks by drawing the statistical character and the edge of the image. The experimental tests show that the proposed approach is robust to image processing and statistics attacks, and can be widely used to protect data which are not permitted to be modified.The algorithms for three-dimensional landform data hiding are proposed on the basis of the wavelet theory. The original image is not required in the first algorithm, and varied true DEM data can be reconstructed from the hidden information to greatly improve the efficiency and security of datum transmission. In the second algorithm, a modified line-based wavelet and its coding is performed to keep the terrain figure and hypsography, and the terrain information is compressed into low bit ratio and little memory. A new method is obtained, by which the strength of the embedding data is image-adaptive according to the quantization noise of wavelet by using the wavelet coefficient set which can be embedded into hidden information, coupled with the maximum strength of Just Noticeable Distortion (JND) tolerance of Human Visual System (HVS).The proposed algorithm is highly safe and the algorithm can be made public because the hiding process is achieved by Rijndael code to construct Hash one-way function. For data elevation model, DEM image is decomposed into the texture (high frequency part) and other parts (low frequency part) by combining wavelet and directional empirical model decomposition (DEMD), and the highly precise decomposition of the DEM image is obtained. According to this decomposition, a novel matrix-deviation-based watermarking approach is proposed. The proposed approach is used to keep the terrain figure and hypsography and extract water watermarking from the hidden information without the original image.Based on the wavelet theory , the algorithms for anomaly detection of network traffic are proposed. First, a method for anomaly detection of network traffic called wavelet variance detection method is proposed, which stems from wavelet singularity theory and statistics. The various wavelet functions are applied to detection so that better wavelet functions can be performed. This method is simple and practical. Moreover, information entropy is used to describe the features of network traffic, and the method for anomaly detection of network traffic based on wavelet and information entropy is proposed. As a result, the detection ability of the anomalies is enhanced, and the classification process becomes more convenient. The experimental results show that the method is efficient and accurate.Based on the wavelet and support vector machine theories, the algorithms for identification P2P traffic are proposed. Firstly, a P2P traffic identification method based on twofold features is proposed. By the simultaneous use of the traffic and payload features, this method improves the efficiency and accuracy of the identification, and therefore is promising for its application to real-time detection. Secondly, a P2P traffic identification method based on the support vector machine with wavelet-based kernel function is proposed. With this method, an iterative training process is adopted to achieve better accuracy. Lastly, an identification method for application-level classification is proposed. Experimental results show that this method is efficient, suitable for real-time traffic identification, and the identification accuracy can be improved by manipulated parameters.