Review: machine learning techniques applied to cybersecurity

Machine learning techniques are a set of mathematical models to solve high non-linearity problems of different topics: prediction, classification, data association, data conceptualization. In this work, the authors review the applications of machine learning techniques in the field of cybersecurity describing before the different classifications of the models based on (1) their structure, network-based or not, (2) their learning process, supervised or unsupervised and (3) their complexity. All the capabilities of machine learning techniques are to be regarded, but authors focus on prediction and classification, highlighting the possibilities of improving the models in order to minimize the error rates in the applications developed and available in the literature. This work presents the importance of different error criteria as the confusion matrix or mean absolute error in classification problems, and relative error in regression problems. Furthermore, special attention is paid to the application of the models in this review work. There are a wide variety of possibilities, applying these models to intrusion detection, or to detection and classification of attacks, to name a few. However, other important and innovative applications in the field of cybersecurity are presented. This work should serve as a guide for new researchers and those who want to immerse themselves in the field of machine learning techniques within cybersecurity

Unravelling cell wall formation in the woody dicot stem

Populus is presented as a model system for the study of wood formation (xylogenesis). The formation of wood (secondary xylem) is an ordered developmental process involving cell division, cell expansion, secondary wall deposition, lignification and programmed cell death. Because wood is formed in a variable environment and subject to developmental control, xylem cells are produced that differ in size, shape, cell wall structure, texture and composition. Hormones mediate some of the variability observed and control the process of xylogenesis. High-resolution analysis of auxin distribution across cambial region tissues, combined with the analysis of transgenic plants with modified auxin distribution, suggests that auxin provides positional information for the exit of cells from the meristem and probably also for the duration of cell expansion. Poplar sequencing projects have provided access to genes involved in cell wall formation. Genes involved in the biosynthesis of the carbohydrate skeleton of the cell wall are briefly reviewed. Most progress has been made in characterizing pectin methyl esterases that modify pectins in the cambial region. Specific expression patterns have also been found for expansins, xyloglucan endotransglycosylases and cellulose synthases, pointing to their role in wood cell wall formation and modification. Finally, by studying transgenic plants modified in various steps of the monolignol biosynthetic pathway and by localizing the expression of various enzymes, new insight into the lignin biosynthesis in planta has been gained.Journal ArticleResearch Support, Non-U.S. Gov'tReviewinfo:eu-repo/semantics/publishe