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A wide range of IDS implementations with anomaly detection modules have been deployed. In general, those modules depend on intrusion knowledge databases, such as Knowledge Discovery Dataset (KDD99), Center for Applied Internet Data Analysis (CAIDA) or Community Resource for Archiving Wireless Data at Dartmouth (CRAWDAD), among others. Once the database is analyzed and a machine learning method is employed to generate detectors, some classes of new detectors are created. Thereafter, detectors are supposed to be deployed in real network environments in order to achieve detection with good results for false positives and detection rates. Since the traffic behavior is quite different according to the user's network activities over available services, restrictions and applications, it is supposed that behavioral-based detectors are not well suited to all kind of networks. This paper presents the differences of detection results between some network scenarios by applying traditional detectors that were calculated with artificial neural networks. The same detector is deployed in different scenarios to measure the efficiency or inefficiency of static training detectors.

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Introducción: El desarrollo de tecnologías móviles ha facilitado la creación de aplicaciones mHealth, las cuales son consideradas herramientas clave para la atención segura y de calidad a los pacientes de poblaciones apartadas y con carencia de infraestructura para la prestación de servicios de salud. El artículo considera una propuesta de un modelo de evaluación que permite determinar las debilidades y vulnerabilidades a nivel de seguridad y calidad de servicio (QoS) en aplicaciones mHealth. Objetivo: Realizar una aproximación de un modelo de análisis que apoye la toma de decisiones referentes al uso y producción de aplicaciones seguras, minimizando el impacto y la probabilidad de ocurrencia de los riesgos de seguridad informática. Materiales y métodos: El tipo de investigación aplicada es de tipo descriptivo, debido a que se detallan cada una las características que deben tener las aplicaciones móviles de salud para alcanzar un nivel de seguridad óptimo. La metodología utiliza las normas que regulan las aplicaciones y las mezcla con técnicas de análisis de seguridad, empleando la caracterización de riesgos planteadas por Open Web Application Security Project -OWASP y las exigencias de QoS de la Unión Internacional de Telecomunicaciones -UIT. Resultados: Se obtuvo un análisis efectivo en aplicaciones reales actuales, lo que muestra sus debilidades y los aspectos a corregir para cumplir con parámetros de seguridad adecuados.  Conclusiones: El modelo permite evaluar los requerimientos de seguridad y calidad de servicio (QoS) de aplicaciones móviles para la salud que puede ser empleado para valorar aplicaciones actuales o generar los criterios antes de su despliegue.

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ObjectivesDue to the uncontrolled increase of the mobile health applications and their scarce use by elderly for reason of absence credibility of measurements by lack scientific support, the aim of this study was to evaluate the differences between the biophysical measurements based on standard instrument against a mobile application using controlled experiments with elderly to propose an effective validation model of the developed apps.MethodsThe subjects of the study (50 people) were elderly people who wanted to check their weight and cardiac status. For this purpose, two mobile applications were used to measure energy expenditure based on physical activity (Activ) and heart rate (SMCa) during controlled walking at specific speeds. Minute-by-minute measurements were recorded to evaluate the average error and the accuracy of the data acquired through confidence intervals by means of statistical analysis of the data.ResultsThe experimental results obtained by the Activ/SMCa apps showed a consistent statistical similarity with those obtained by specialized equipment with confidence intervals of 95%. All the subjects were advised and trained on the use of the applications, and the initial registration of data to characterize them served to significantly affect the perceived ease of use.ConclusionsThis is the first model to validate a health-app with elderly people allowed to demonstrate the anthropometric and body movement differences of subjects with equal body mass index (BMI) but younger. Future studies should consider not only BMI data but also other variables, such as age and usability perception factors.