Biblio

This paper describes an approach to detecting malicious code introduced by insiders, which can compromise the data integrity in a program. The approach identifies security spots in a program, which are either malicious code or benign code. Malicious code is detected by reviewing each security spot to determine whether it is malicious or benign. The integrity breach conditions (IBCs) for object-oriented programs are specified to identify security spots in the programs. The IBCs are specified by means of the concepts of coupling within an object or between objects. A prototype tool is developed to validate the approach with a case study.

Coupling is a common approach for constructing new chaotic systems. In this paper, I present a novel way of coupling, which is utilized to construct a new chaotic system. Afterwards, the system is analyzed in detail and a pseudorandom bit generator is proposed based on it. Next, I employ five statistic tests to evaluate the pseudo randomness of generated sequences. Linear complexity and cipher space are analyzed at last. All the results demonstrate that the proposed generator possesses excellent properties.

There is an inevitable trade-off between spatial and spectral resolutions in optical remote sensing images. A number of data fusion techniques of multimodal images with different spatial and spectral characteristics have been developed to generate optical images with both spatial and spectral high resolution. Although some of the techniques take the spectral and spatial blurring process into account, there is no method that attempts to retrieve an optical image with both spatial and spectral high resolution, a spectral blurring filter and a spectral response simultaneously. In this paper, we propose a new framework of spatial resolution enhancement by a fusion of multiple optical images with different characteristics based on tensor decomposition. An optical image with both spatial and spectral high resolution, together with a spatial blurring filter and a spectral response, is generated via canonical polyadic (CP) decomposition of a set of tensors. Experimental results featured that relatively reasonable results were obtained by regularization based on nonnegativity and coupling.