Biblio
This work is motivated by the rapid increase of the number of attacks in computer networks and software engineering. In this paper we study identity snowball attacks and formally prove the correctness of suggested solutions to this type of attack (solutions that are based on the graph reachability reduction) using a proof assistant. We propose a model of an attack graph that captures technical informations about the calculation of reachability of the graph. The model has been implemented with the proof assistant PVS 6.0 (Prototype Verification System). It makes it possible to prove algorithms of reachability reduction such as Sparsest\_cut.
Nowadays, the principle of image mining plays a vital role in various areas of our life, where numerous frameworks based on image mining are proposed for object recognition, object tracking, sensing images and medical image diagnosis. Nevertheless, the research in the image authentication based on image mining is still confined. Therefore, this paper comes to present an efficient engagement between the frequent pattern mining and digital watermarking to contribute significantly in the authentication of images transmitted via public networks. The proposed framework exploits some robust features of image to extract the frequent patterns in the image data. The maximal relevant patterns are used to discriminate between the textured and smooth blocks within the image, where the texture blocks are more appropriate to embed the secret data than smooth blocks. The experiment's result proves the efficiency of the proposed framework in terms of stabilization and robustness against different kind of attacks. The results are interesting and remarkable to preserve the image authentication.