Detection of algorithmically generated malicious domain names using masked N-grams

Malware detection is a challenge that has increased in complexity in the last few years. A widely adopted strategy is to detect malware by means of analyzing network traffic, capturing the communications with their command and control (C&C) servers. However, some malware families have shifted to a stealthier communication strategy, since anti-malware companies maintain blacklists of known malicious locations. Instead of using static IP addresses or domain names, they algorithmically generate domain names that may host their C&C servers. Hence, blacklist approaches become ineffective since the number of domain names to block is large and varies from time to time. In this paper, we introduce a machine learning approach using Random Forest that relies on purely lexical features of the domain names to detect algorithmically generated domains. In particular, we propose using masked N-grams, together with other statistics obtained from the domain name. Furthermore, we provide a dataset built for experimentation that contains regular and algorithmically generated domain names, coming from different malware families. We also classify these families according to their type of domain generation algorithm. Our findings show that masked N-grams provide detection accuracy that is comparable to that of other existing techniques, but with much better performance

Support vector machines framework for linear signal processing

This paper presents a support vector machines (SVM) framework to deal with linear signal processing (LSP) problems. The approach relies on three basic steps for model building: (1) identifying the suitable base of the Hilbert signal space in the model, (2) using a robust cost function, and (3) minimizing a constrained, regularized functional by means of the method of Lagrange multipliers. Recently, autoregressive moving average (ARMA) system identification and non-parametric spectral analysis have been formulated under this framework. The generalized, yet simple, formulation of SVM LSP problems is particularized here for three different issues: parametric spectral estimation, stability of Infinite Impulse Response filters using the gamma structure, and complex ARMA models for communication applications. The good performance shown on these different domains suggests that other signal processing problems can be stated from this SVM framework.Publicad

Development of Antibody-Coated Magnetite Nanoparticles for Biomarker Immobilization

Magnetic nanoparticles (MNPs) have great potential in biomedical applications because of their magnetic response offers the possibility to direct them to specific areas and target biological entities. Magnetic separation of biomolecules is one of the most important applications of MNPs because their versatility in detecting cancer biomarkers. However, the effectiveness of this method depends on many factors, including the type of functionalization onto MNPs. Therefore, in this study, magnetite nanoparticles have been developed in order to separate the 5′-nucleotidase enzyme (5eNT). The 5eNT is used as a bio-indicator for diagnosing diseases such as hepatic ischaemia, liver tumor, and hepatotoxic drugs damage. Magnetic nanoparticles were covered in a core/shell type with silica, aminosilane, and a double shell of silica-aminosilane. A ScFv (fragment antibody) and anti-CD73 antibody were attached to the coated nanoparticles in order to separate the enzyme. The magnetic separation of this enzyme with fragment antibody was found to be 28% higher than anti-CD73 antibody and the enzyme adsorption was improved with the double shell due to the increased length of the polymeric chain. Magnetite nanoparticles with a double shell (silica-aminosilane) were also found to be more sensitive than magnetite with a single shell in the detection of biomarkers

p21(Cip1) plays a critical role in the physiological adaptation to fasting through activation of PPARα.

Fasting is a physiological stress that elicits well-known metabolic adaptations, however, little is known about the role of stress-responsive tumor suppressors in fasting. Here, we have examined the expression of several tumor suppressors upon fasting in mice. Interestingly, p21 mRNA is uniquely induced in all the tissues tested, particularly in liver and muscle (>10 fold), and this upregulation is independent of p53. Remarkably, in contrast to wild-type mice, p21-null mice become severely morbid after prolonged fasting. The defective adaptation to fasting of p21-null mice is associated to elevated energy expenditure, accelerated depletion of fat stores, and premature activation of protein catabolism in the muscle. Analysis of the liver transcriptome and cell-based assays revealed that the absence of p21 partially impairs the transcriptional program of PPARα, a key regulator of fasting metabolism. Finally, treatment of p21-null mice with a PPARα agonist substantially protects them from their accelerated loss of fat upon fasting. We conclude that p21 plays a relevant role in fasting adaptation through the positive regulation of PPARα

Rubidium metal target development for large scale 82Sr production: LA-UR-14-22338

Strontium-82 (t1/2 = 25.5 d) is one of the medical isotopes produced on a large scale at the Isotope Production Facility (IPF) of the Los Alamos National Laboratory (LANL), employing a high intensity 100 MeV proton beam and RbCl targets. A constant increase in the 82Sr demand over the last decade combined with an established thermal limit of molten RbCl salt targets [1,2] has challenged the IPF’s world leading production capacity in recent years and necessitated the consideration of low-melting point (39.3 °C) Rb metal targets. Metal targets are used at other facilities [3–5] and offer obvious production rate advantages due to a higher relative density of Rb target atoms and a higher expected thermal performance of molten metal. One major disadvantage is the known violent reaction of molten Rb with cooling water and the potential for facility damage following a catastrophic target failure. This represents a significant risk, given the high beam intensities used routinely at IPF. In order to assess this risk, a target failure experiment was conducted at the LANL firing site using a mockup target station. Subsequent fabrication, irradiation and processing of two prototype targets showed a target thermal performance consistent with thermal modeling predictions and yields in agreement with predictions based on IAEA recommended cross sections [6]. Target failure test: The target failure test bed (FIG. 1) was constructed to represent a near replica of the IPF target station, incorporating its most important features. One of the most vulnerable components in the assembly is the Inconel beam window (FIG. 2) which forms the only barrier between the target cooling water and the beam line vacuum. The test bed also mimicked relevant IPF operational parameters seeking to simulate the target environment during irradiation, such as typical cooling water flow velocities around the target surfaces. While the aggressive thermal effects of the beam heating could not be simulated directly, heated cooling water (45 °C) ensured that the rubidium target material remained molten during the failure test. A worst case catastrophic target failure event was initiated by uncovering an oversized predrilled pinhole (1 mm Φ) to abruptly expose the molten target material to fast flowing cooling water. Prototype target irradiations: Two prototype Rb metal target containers were fabricated by machining Inconel 625 parts and by EB welding. The target containers were filled with molten Rb metal under an inert argon atmosphere. Follow-ing appropriate QA inspections, the prototype targets were irradiated in the medium energy slot of a standard IPF target stack using beam currents up to 230 µA. After irradiation the targets were transported to the LANL hot cell facili-ty for processing and for 82Sr yield verification. During the target failure test, cooling water conductivity and pressure excursions in the target chamber were continuously monitored and recorded at a rate of 1 kHz. Video footage taken of the beam window and the pinhole area combined with the recorded data indicated an aggressive reaction between the Rb metal and the cooling water, but did not reveal a violent explosion that could seriously damage the beam window. These observations, together with thermal model predictions, provided the necessary confidence to fabricate and fill prototype targets for irradiation at production-scale beam currents. X-ray imaging of filled targets (FIG. 3) shows a need for tighter control over the target fill level. One prototype target was first subjected to lower intensity (< 150 µA) beams before the second was irradiated at production level (230 µA) beams. During irradiation, monitoring of cooling water conductivity indicated no container breach or leak and, as anticipated given the model predictions, the post irradiation target inspection showed no sign of imminent thermal failure (see FIG. 4). Subsequent chemical processing of the targets followed an established procedure that was slightly modified to accommodate the larger target mass. TABLE 1 shows that post chemistry 82Sr yields agree to within 2 % of the in-target production rates expected on the basis of IAEA recommended cross sections. The table also compares 82Sr yields from the Rb metal targets against yields routinely obtained from RbCl targets, showing an increase in yield of almost 50 %

The experiences of mothers of children and young people with intellectual disabilities during the first COVID‐19 lockdown period

Abstract: Background: Recent COVID‐19 lockdown restrictions resulted in reduced access to educational, professional and social support systems for children with intellectual disabilities and their carers. Aim: The aim of this study was to gain insight into the ways mothers of children with intellectual disabilities coped during the first 2020 lockdown period. Methods: Eight mothers of children with intellectual disabilities were interviewed. The recordings of these interviews were subjected to a thematic analysis. Results: Three main themes were identified: carrying the burden; a time of stress; and embracing change and looking to the future. Conclusions: All mothers experienced increased burden and stress. However, some also described some positive impact of lockdown conditions on them as well as on their child's well‐being and behaviour. These findings are discussed in the light of the (Journal of Applied Research in Intellectual Disabilities, 33, 2020, 1523) survey results on parental coping and suggestions for future service provision during pandemic conditions are proposed

Estimación del nivel de habilidad en sistemas tutores inteligentes utilizando una metodología multiatributo

Para el funcionamiento ideal de un sistema tutor inteligente es indispensable poder estimar el nivel de habilidad de los estudiantes de acuerdo a objetivos complejos de aprendizaje. En este trabajo se propone una arquitectura para la evaluación del nivel de habilidad del estudiante, basada en la teoría de la utilidad multiatributo, utilizando como operador de agregación a la integral de Choquet. El método toma en cuenta los objetivos de aprendizaje planteados por el tomador de decisiones (académicos, representantes instituciones, etc.) representados por relaciones complejas que se pueden dar entre los criterios considerados para la evaluación.XVI Workshop Tecnología Informática Aplicada en Educación (WTIAE).Red de Universidades con Carreras en Informática (RedUNCI