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Digital security plays an ever-increasing, crucial role in today’s information-based society. The variety of threats and attack patterns has dramatically increased with the advent of digital transformation in our lives. Researchers in both public and private sectors have tried to identify new means to counteract these threats, seeking out-of-the-box ideas and novel approaches. Amongst these, data analytics and artificial intelligence/machine learning tools seem to gain new ground in digital defence. However, such instruments are used mainly offline with the purpose of auditing existing IDS/IDPS solutions. We submit a novel concept for integrating machine learning and analytical tools into a live intrusion detection and prevention solution. This approach is named the Experimental Cyber Attack Detection Framework (ECAD). The purpose of this framework is to facilitate research of on-the-fly security applications. By integrating offline results in real-time traffic analysis, we could determine the type of network access as a legitimate or attack pattern, and discard/drop the latter. The results are promising and show the benefits of such a tool in the early prevention stages of both known and unknown cyber-attack patterns.

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A fractal medium is de¯ned and its computational properties (bistability, reproduction itself, capacity to memorize, self-similarity, polarization, information communication, etc.) are presented and argued. As it is well known, the quantum medium is the base of the theoretical fundamentation of the quantum computer. We prove that the fractal medium is an extension of the quantum medium. Hence, the fractal computer can be de¯ned as generalization of the quantum computer.

In this paper we propose and compare two methods to optimize the numerical computations for the diffusion term in a nonlocal formulation for a reaction-diffusion equation. The diffusion term is particularly computationally intensive due to the integral formulation, and thus finding a better way of computing its numerical approximation could be of interest, given that the numerical analysis usually takes place on large input domains having more than one dimension. After introducing the general reaction-diffusion model, we discuss a numerical approximation scheme for the diffusion term, based on a finite difference method. In the next sections we propose two algorithms to solve the numerical approximation scheme, focusing on finding a way to improve the time performance. While the first algorithm (sequential) is used as a baseline for performance measurement, the second algorithm (parallel) is implemented using two different memory-sharing parallelization technologies: Open Multi-Processing (OpenMP) and CUDA. All the results were obtained by using the model in image processing applications such as image restoration and segmentation.

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A sure method for a business organization to sell more products is to expand its customer base and to have its products recommended by other organizations and individuals. This paper takes a look at the techniques used by shopping websites in order to entice the user in purchasing their products, and proposes a system for recommending products and services provided by different online businesses to potential customers. The solution is built upon a service-oriented architecture that allows businesses to share information regarding customers’ purchases while taking into account the user privacy issue. Intelligent agents, which rely on a product type association dynamically weighted graph, are employed in order to obtain and to process the information needed to make the suggestions. The use of intelligent agents significantly improves the quality of the recommendations made by the system. This improvement is achieved by suggesting products and services depending on other users’ purchasing patterns while also considering the different product types and quantities sold by the business organizations that are part of the system.

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