A Methodology for Evaluating the Robustness of Anomaly Detectors to Adversarial Attacks in Industrial Scenarios

Anomaly Detection systems based on Machine and Deep learning are the most promising solutions to detect cyberattacks in the industry. However, these techniques are vulnerable to adversarial attacks that downgrade prediction performance. Several techniques have been proposed to measure the robustness of Anomaly Detection in the literature. However, they do not consider that, although a small perturbation in an anomalous sample belonging to an attack, i.e., Denial of Service, could cause it to be misclassified as normal while retaining its ability to damage, an excessive perturbation might also transform it into a truly normal sample, with no real impact on the industrial system. This paper presents a methodology to calculate the robustness of Anomaly Detection models in industrial scenarios. The methodology comprises four steps and uses a set of additional models called support models to determine if an adversarial sample remains anomalous. We carried out the validation using the Tennessee Eastman process, a simulated testbed of a chemical process. In such a scenario, we applied the methodology to both a Long-Short Term Memory (LSTM) neural network and 1-dimensional Convolutional Neural Network (1D-CNN) focused on detecting anomalies produced by different cyberattacks. The experiments showed that 1D-CNN is significantly more robust than LSTM for our testbed. Specifically, a perturbation of 60% (empirical robustness of 0.6) of the original sample is needed to generate adversarial samples for LSTM, whereas in 1D-CNN the perturbation required increases up to 111% (empirical robustness of 1.11)

Leptogenesis and rescattering in supersymmetric models

The observed baryon asymmetry of the Universe can be due to the $B-L$ violating decay of heavy right handed (s)neutrinos. The amount of the asymmetry depends crucially on their number density. If the (s)neutrinos are generated thermally, in supersymmetric models there is limited parameter space leading to enough baryons. For this reason, several alternative mechanisms have been proposed. We discuss the nonperturbative production of sneutrino quanta by a direct coupling to the inflaton. This production dominates over the corresponding creation of neutrinos, and it can easily (i.e. even for a rather small inflaton-sneutrino coupling) lead to a sufficient baryon asymmetry. We then study the amplification of MSSM degrees of freedom, via their coupling to the sneutrinos, during the rescattering phase which follows the nonperturbative production. This process, which mainly influences the (MSSM) $D-$flat directions, is very efficient as long as the sneutrinos quanta are in the relativistic regime. The rapid amplification of the light degrees of freedom may potentially lead to a gravitino problem. We estimate the gravitino production by means of a perturbative calculation, discussing the regime in which we expect it to be reliable.Comment: (20 pages, 6 figures), references added, typos corrected. Final version in revte

Nurses' perceptions of aids and obstacles to the provision of optimal end of life care in ICU

Contains fulltext : 172380.pdf (publisher's version ) (Open Access

A Methodology for Evaluating the Robustness of Anomaly Detectors to Adversarial Attacks in Industrial Scenarios

Anomaly Detection systems based on Machine and Deep learning are the most promising solutions to detect cyberattacks in the industry. However, these techniques are vulnerable to adversarial attacks that downgrade prediction performance. Several techniques have been proposed to measure the robustness of Anomaly Detection in the literature. However, they do not consider that, although a small perturbation in an anomalous sample belonging to an attack, i.e., Denial of Service, could cause it to be misclassified as normal while retaining its ability to damage, an excessive perturbation might also transform it into a truly normal sample, with no real impact on the industrial system. This paper presents a methodology to calculate the robustness of Anomaly Detection models in industrial scenarios. The methodology comprises four steps and uses a set of additional models called support models to determine if an adversarial sample remains anomalous. We carried out the validation using the Tennessee Eastman process, a simulated testbed of a chemical process. In such a scenario, we applied the methodology to both a Long-Short Term Memory (LSTM) neural network and 1-dimensional Convolutional Neural Network (1D-CNN) focused on detecting anomalies produced by different cyberattacks. The experiments showed that 1D-CNN is significantly more robust than LSTM for our testbed. Specifically, a perturbation of 60% (empirical robustness of 0.6) of the original sample is needed to generate adversarial samples for LSTM, whereas in 1D-CNN the perturbation required increases up to 111% (empirical robustness of 1.11)

AulaEnergía ETSIME-UPM. Un espacio colaborativo de difusión, formación tecnológica y desarrollo de la energía: Geotermia

En los últimos años la configuración de las Titulaciones que tenemos en nuestro Centro ha evolucionado hacia un carácter más energético. Si bien es cierto, que los temas energéticos siempre han estado dentro de las atribuciones profesionales de los Ingenieros de Minas, estos nuevos aires nos han hecho modificar algunas de las infraestructuras docentes de nuestra Escuela. En esta comunicación se plantea el desarrollo de un proyecto centrado en el estudio de las diferentes energías alternativas que están incluidas en nuestros planes docentes, proponiendo la incorporación de la geotermia para suplir los usos convencionales de climatización y ACS. Esta iniciativa se materializará en el AulaEnergía, una incubadora de ideas y proyectos, que además permita a los visitantes informarse sobre la tecnología y sentir los beneficios del aprovechamiento geotérmico en el mismo lugar

AulaEnergía ETSIME-UPM. Un espacio colaborativo de difusión, formación tecnológica y desarrollo de la energía: Geotermia

En los últimos años la configuración de las Titulaciones que tenemos en nuestro Centro ha evolucionado hacia un carácter más energético. Si bien es cierto, que los temas energéticos siempre han estado dentro de las atribuciones profesionales de los Ingenieros de Minas, estos nuevos aires nos han hecho modificar algunas de las infraestructuras docentes de nuestra Escuela. En esta comunicación se plantea el desarrollo de un proyecto centrado en el estudio de las diferentes energías alternativas que están incluidas en nuestros planes docentes, proponiendo la incorporación de la geotermia para suplir los usos convencionales de climatización y ACS. Esta iniciativa se materializará en el AulaEnergía, una incubadora de ideas y proyectos, que además permita a los visitantes informarse sobre la tecnología y sentir los beneficios del aprovechamiento geotérmico en el mismo lugar

Lipid and protein maps defining arterial layers in atherosclerotic aorta

Subclinical atherosclerosis cannot be predicted and novel therapeutic targets are needed. The molecular anatomy of healthy and atherosclerotic tissue is pursued to identify ongoing molecular changes in atherosclerosis development. Mass Spectrometry Imaging (MSI) accounts with the unique advantage of analyzing proteins and metabolites (lipids) while preserving their original localization; thus two dimensional maps can be obtained. Main molecular alterations were investigated in a rabbit model in response to early development of atherosclerosis. Aortic arterial layers (intima and media) and calcified regions were investigated in detail by MALDI-MSI and proteins and lipids specifically defining those areas of interest were identified. These data further complement main findings previously published in J Proteomics (M. Martin-Lorenzo et al., J. Proteomics. (In press); M. Martin-Lorenzo et al., J. Proteomics 108 (2014) 465–468.) [1,2]

Molecular anatomy of ascending aorta in atherosclerosis by MS Imaging: Specific lipid and protein patterns reflect pathology

Proteomic

Informe Taller Pesquerias Artesanales y Recreativas

En el taller se han realizado más de 20 presentaciones acerca de los estudios sobre las pesquerías artesanales y recreativas en aguas de la ZEE de España. En base a ello y al conocimiento de los expertos reunidos se han definido dos planes de seguimiento para obtener la huella pesquera de las pesquerías profesionales sin VMS, uno con sistemas de seguimientos de localización en tres fases y otro con observadores a bordo y encuestas en puntos de primera venta en dos fases. En el caso de las actividades pesqueras no profesionales (recreativas) se ha propuesto un plan de seguimiento basado en encuestas telefónicas y presenciales en tres fases. Además se decidió incluir las actividades pelágicas y/o de superficie dentro de los programas de seguimiento