Proposing to use artificial neural Networks for NoSQL attack detection

[EN] Relationships databases have enjoyed a certain boom in software worlds until now. These days, with the rise of modern applications, unstructured data production, traditional databases do not completely meet the needs of all systems. Regarding these issues, NOSQL databases have been developed and are a good alternative. But security aspects stay behind. Injection attacks are the most serious class of web attacks that are not taken seriously in NoSQL. This paper presents a Neural Network model approach for NoSQL injection. This method attempts to use the best and most effective features to identify an injection. The features used are divided into two categories, the first one based on the content of the request, and the second one independent of the request meta parameters. In order to detect attack payloads features, we work on character level analysis to obtain malicious rate of user inputs. The results demonstrate that our model has detected more attack payloads compare with models that work black list approach in keyword level

SELECTIVIDAD DE LAS ISOFORMAS DE 140-180 KDA DE NCAM EN LA PAPILA ÓPTICA DEL EMBRIÓN DE POLLO

During embryonic development of chicken retina and retinotectal proyection, neural cells showed spatiotemporal patterns in the glycosidic residues located in the plasmatic membranes. Those glycidic components are related with adhesion and recognition cellular behaviours during the generation of nervous system cytoarchitecture. Thus those sugars are included both in usual glycoproteins as in a group of molecules called as cell adhesion molecules (CAM). The first isolated molecule in this group was the neural cell adhesion molecule (NCAM). This glycoprotein has a proteic core with diverse isoforms and it has a variable sialic acids chain. It was able to determine that 120 kDa NCAM isoform shows scarcely sialized chains in intermediate stages by using conventional techniques, lectins and immunohistochemistry. Meanwhile 140-180 kDa NCAM isoforms possess high content in sialic acid and they are present both in early and final stages. Other authors reported that human 120 kDa NCAM isoform was similar to that observed in the Gallus domesticus retina. Our results showed that 140- 180kDa isoforms were only presented in the nerve optic fibers when they were left the optic disc.Durante la embriogénesis de la retina del Gallus domesticus, los neuroblastos muestran variaciones temporales y espaciales de los residuos glucosilados a nivel de sus membranas plasmáticas. Se considera que estos componentes glucídicos están implicados en la organización de estructuras neurales inmaduras, mediante su participación en mecanismos de reconocimiento y adhesión celulares. Los componentes glucosilados van a formar parte de glucoproteínas convencionales, así como de un grupo de moléculas a las que genéricamente se han denominado moléculas de adhesión celular. La primera de estas moléculas aislada fue la neural cell adhesion molecule (NCAM), glucoproteína compuesta por un armazón proteico con diversas isoformas y una cadena más o menos variable de polímeros de ácidos siálicos. La combinación de técnicas convencionales his- toquímicas, de lectinas e inmunocitoquímicas han permitido determinar que las moléculas de NCAM de 120 kDa presentan cadenas pobremente sializadas en estadíos intermedios del desarrollo, mientras en estadíos tempranos y finales los ácidos siálicos están más abundantemente representados en las isoformas de 140-180 kDa. Algunos autores han determinado que la isoforma de NCAM presente durante el desarrollo de la retina del Gallus domesticus es similar a la humana de 120 kDa. Nuestros resultados indican que las isoformas de 140-180 kDa sólo se presentan en las fibras del nervio óptico una vez que estas abandonan la papila óptica

An insight into imbalanced Big Data classification: outcomes and challenges

Big Data applications are emerging during the last years, and researchers from many disciplines are aware of the high advantages related to the knowledge extraction from this type of problem. However, traditional learning approaches cannot be directly applied due to scalability issues. To overcome this issue, the MapReduce framework has arisen as a “de facto” solution. Basically, it carries out a “divide-and-conquer” distributed procedure in a fault-tolerant way to adapt for commodity hardware. Being still a recent discipline, few research has been conducted on imbalanced classification for Big Data. The reasons behind this are mainly the difficulties in adapting standard techniques to the MapReduce programming style. Additionally, inner problems of imbalanced data, namely lack of data and small disjuncts, are accentuated during the data partitioning to fit the MapReduce programming style. This paper is designed under three main pillars. First, to present the first outcomes for imbalanced classification in Big Data problems, introducing the current research state of this area. Second, to analyze the behavior of standard pre-processing techniques in this particular framework. Finally, taking into account the experimental results obtained throughout this work, we will carry out a discussion on the challenges and future directions for the topic.This work has been partially supported by the Spanish Ministry of Science and Technology under Projects TIN2014-57251-P and TIN2015-68454-R, the Andalusian Research Plan P11-TIC-7765, the Foundation BBVA Project 75/2016 BigDaPTOOLS, and the National Science Foundation (NSF) Grant IIS-1447795

Single-solution Simulated Kalman Filter algorithm for global optimisation problems

This paper introduces single-solution Simulated Kalman Filter (ssSKF), a new single-agent optimisation algorithm inspired by Kalman Filter, for solving real-valued numerical optimisation problems. In comparison, the proposed ssSKF algorithm supersedes the original population-based Simulated Kalman Filter (SKF) algorithm by operating with only a single agent, and having less parameters to be tuned. In the proposed ssSKF algorithm, the initialisation parameters are not constants, but they are produced by random numbers taken from a normal distribution in the range of [0, 1], thus excluding them from tuning requirement. In order to balance between the exploration and exploitation in ssSKF, the proposed algorithm uses an adaptive neighbourhood mechanism during its prediction step. The proposed ssSKF algorithm is tested using the 30 benchmark functions of CEC 2014, and its performance is compared to that of the original SKF algorithm, Black Hole (BH) algorithm, Particle Swarm Optimisation (PSO) algorithm, Grey Wolf Optimiser (GWO) algorithm and Genetic Algorithm (GA). The results show that the proposed ssSKF algorithm is a promising approach and able to outperform GWO and GA algorithms, significantly