Traffic Analysis on Mobile Devices Using Supervised Deep Learning Techniques

Trabajo de Fin de Grado en Ingeniería Informática, Facultad de Informática UCM, Departamento de Ingeniería del Software e Inteligencia Artificial, Curso 2021/2022.En los últimos años, el tráfico entre aplicaciones móviles ha crecido exponencialmente. Así, la tarea de clasificar el tráfico en la red, como puede ser indicar qué aplicación móvil ha generado dicho tráfico, se ha vuelto cada vez más difícil por el elevado uso de protocolos de seguridad para proteger la privacidad de los usuarios y de los datos. Los métodos de clasificación de tráfico basados en el aprendizaje profundo pueden hacer frente a este impedimento y por esta razón el sistema propuesto en este trabajo está basado en autocodificadores, redes neuronales convolucionales y redes neuronales convolucionales gráficas. El conjunto de datos de tráfico con el que se han hecho los experimentos, se ha recopilado durante todo el periodo de realización de este trabajo por un grupo de alumnos y sobre un conjunto de aplicaciones móviles existentes en Android.In recent years, traffic between mobile applications has grown exponentially. Thus, the task of classifying traffic on the network, such as indicating which mobile application has generated the traffic, has become increasingly difficult due to the high use of security protocols to protect user and data privacy. Deep learning based traffic classification methods can cope with this impediment and for this reason the system proposed in this work is based on autoencoders, convolutional neural networks and graphical convolutional neural networks. The traffic dataset with which the experiments have been done, have been collected throughout the period of this work by a group of students and on a set of existing mobile applications on Android.Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEunpu

Automatic detection of fraudulent websites

Trabajo de Fin de Grado en Ingeniería Informática, Facultad de Informática UCM, Departamento de Ingeniería del Software e Inteligencia Artificial, Curso 2019/2020.A lo largo de los últimos años se ha observado un aumento considerable en las comunicaciones y operaciones que se realizan diariamente a través de Internet. Las redes sociales o el comercio electrónico son un ejemplo del tipo de gestiones que se pueden llevar a cabo en la red. Este aumento ha supuesto que cada año sean más frecuentes los ataques de phishing. Estos ataques utilizan ingeniería social para robar información personal o confidencial al usuario, haciéndose pasar por una empresa o persona de confianza. Durante la pandemia declarada por el brote de Coronavirus Desease (COVID19), debido al aumento del teletrabajo y de las compras en línea, este tipo de ataques se ha incrementado en un 5.38% [APWG20], con un máximo de 59,525 sitios web fraudulentos detectados en un solo día. Por eso cada día es más importante el desarrollo de herramientas que permitan detectar estos ataques. Actualmente existen sistemas de detección basados en listas negras que son muy potentes, pero que no tienen la capacidad de detectar páginas web de phishing en tiempo real, algo necesario cuando la duración media de una página web de phishing es en torno a 20 horas [MC07]. También, existen sistemas de detección basados en algoritmos de aprendizaje automático, que extraen características de las páginas web de phishing y desarrollan un modelo que permite predecir si una página web es maliciosa o no. Este tipo de sistemas de detección permite identificar páginas web fraudulentas en tiempo real. Este trabajo propone un sistema de detección que combina ambos métodos. Primero se comprueba que la página web sospechosa no está en la lista negra localizada en una base de datos almacenada localmente. En caso de no ser encontrada se realiza una búsqueda en la base de datos de Google Safe Browsing. Si la respuesta es negativa se utiliza un modelo de predicción para categorizar la página como phishing o no phishing. El modelo ha sido seleccionado tras probar 12 algoritmos diferentes de aprendizaje automático a los cuales se les ha suministrado características extraídas de la dirección de la página web y del modelo de objeto de documento. Posteriormente se comparan los resultados del modelo con un conjunto de trabajos seleccionados. El mejor resultado se ha obtenido con el algoritmo de Bosques aleatorios o Random Forest. Se ha logrado un porcentaje de aciertos del 90.6%, un porcentaje de falsos positivos del 2.35% y una precisión de 95,50%.Over the last few years, there has been a considerable increase in communications and operations carried out through the Internet. Social networks or electronic commerce are an example of the type of management that can be carried out online. This increase is reflected in the fact that fraud attacks are more frequent every year. These attacks use social engineering strategies to steal sensitive information from the users pretending to be a trusted company or person. During the pandemic declared by the COVID-19 outbreak due to the increase of telecommuting and online shopping, these type of attacks have increased by 5.38% [APWG20] with a maximum of 59,525 fraudulent websites detected in a single day. That is why the development of tools that detect phishing attacks has never been more important than it is now. There are currently blacklist detection systems that are very powerful, but do not have the ability to detect phishing web pages in real time, something necessary when the average duration of a phishing web page is around 20 hours [MC07]. There are also detection systems based on machine learning algorithms, which extract features from phishing web pages and, through machine learning algorithms, develop a model that allows predicting whether a web page is malicious or not. This type of detection systems allow to detect phishing web pages in real time. We propose a detection system that combines both systems. First we check that the suspicious web page is not on our blacklist, which is localized in our database. If it is not found, we search it in the Google Safe Browsing database. If the answer is negative, we use a prediction model to categorize the page as phish or non-phish. The model has been selected after testing 12 different machine learning algorithms which have been provided with features extracted from the web page address and the document object model. Later, we compare the results of the model with a set of selected papers. The best result has been obtained using the Random Forest algorithm. We achieved a percentage of true positives of 90.6% a percentage of false positives of 2.35% and a percentage of accuracy of 95,50%.Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEunpu

Improved functionalization of oleic acid-coated iron oxide nanoparticles for biomedical applications

Superparamagnetic iron oxide nanoparticles can providemultiple benefits for biomedical applications in aqueous environments such asmagnetic separation or magnetic resonance imaging. To increase the colloidal stability and allow subsequent reactions, the introduction of hydrophilic functional groups onto the particles’ surface is essential. During this process, the original coating is exchanged by preferably covalently bonded ligands such as trialkoxysilanes. The duration of the silane exchange reaction, which commonly takes more than 24 h, is an important drawback for this approach. In this paper, we present a novel method, which introduces ultrasonication as an energy source to dramatically accelerate this process, resulting in high-quality waterdispersible nanoparticles around 10 nmin size. To prove the generic character, different functional groups were introduced on the surface including polyethylene glycol chains, carboxylic acid, amine, and thiol groups. Their colloidal stability in various aqueous buffer solutions as well as human plasma and serum was investigated to allow implementation in biomedical and sensing applications.status: publishe

Emphysema Predicts Hospitalisation and Incident Airflow Obstruction among Older Smokers: A Prospective Cohort Study

Emphysema on CT is common in older smokers. We hypothesised that emphysema on CT predicts acute episodes of care for chronic lower respiratory disease among older smokers.Participants in a lung cancer screening study age ≥ 60 years were recruited into a prospective cohort study in 2001-02. Two radiologists independently visually assessed the severity of emphysema as absent, mild, moderate or severe. Percent emphysema was defined as the proportion of voxels ≤ -910 Hounsfield Units. Participants completed a median of 5 visits over a median of 6 years of follow-up. The primary outcome was hospitalization, emergency room or urgent office visit for chronic lower respiratory disease. Spirometry was performed following ATS/ERS guidelines. Airflow obstruction was defined as FEV1/FVC ratio <0.70 and FEV1<80% predicted.Of 521 participants, 4% had moderate or severe emphysema, which was associated with acute episodes of care (rate ratio 1.89; 95% CI: 1.01-3.52) adjusting for age, sex and race/ethnicity, as was percent emphysema, with similar associations for hospitalisation. Emphysema on visual assessment also predicted incident airflow obstruction (HR 5.14; 95% CI 2.19-21.1).Visually assessed emphysema and percent emphysema on CT predicted acute episodes of care for chronic lower respiratory disease, with the former predicting incident airflow obstruction among older smokers

Artepharm: Commercialising the Malaria cure in Africa

SMU Faculty/Staff can download the case and teaching note with your SMU login ID and Password via the following links: The Case (SMU-19-0024) Teaching Note (SMU-19-0024TN) For purchase of the case and supplementary materials via The CMP Shop, please access the following links: The Case (SMU-19-0024) For purchase of the case and supplementary materials via The Case Centre, please access the following links: The Case (SMU-19-0024) Teaching Note (SMU-19-0024TN) For purchase of the case and supplementary materials via Harvard Business Publishing, please access the following links: The Case (SMU-19-0024) Teaching Note (SMU-19-0024TN) </ul

The Northern Institute of Taiwan Studies at the University of Central Lancashire: Expanding the Boundaries of Taiwan Studies

The Northern Institute of Taiwan Studies (NorITS) was launched in 2018 and relies on the hard work of a team of five experts in the field. In this report we discuss the accomplishments our colleagues have achieved in these three years and the contributions that NorITS has made to Taiwan studies, with the aim to start a conversation on how to frame Taiwan studies against contemporary challenges and opportunities of academia

Shared decision making: A dual‐layer model to tackling multimorbidity in primary care

It is common for primary care providers (PCPs) to manage complex multimorbidity. When caring for patients with multimorbidity, PCPs face challenges to tackle several issues within a short consultation in order to address patients' complex needs. Furthermore, some PCPs may lack access to a multidisciplinary team and need to manage multimorbidity within the confine of a PCP-patient partnership only. Instead of attempting to address multiple health issues within a single consultation, it would be more feasible and time effective for PCPs and patients to jointly prioritize the health issue to focus on. Using the Malaysian primary care setting as a case study, a dual-layer-shared decision-making approach is proposed whereby PCPs and patients make decisions on which disease(s) (layer 1) and treatment(s) (layer 2) to prioritize. This dual-layer model aims to address the challenges of short consultation time and limited healthcare resources by encouraging PCPs and patients to discuss, negotiate, and agree on the decision during the consultation to ensure patients' health needs are addressed. © 2019 John Wiley & Sons, Ltd

An Intervention to Increase Outdoor Play in Early Childhood Education Centers (PROmoting Early Childhood Outside): Protocol for a Pilot Wait-list Control Cluster Randomized Trial

Background: Participation in outdoor play has been extensively documented as beneficial for the health, well-being, and development of children. Canadian early childhood education centers (ECECs) are important settings in young children’s lives and provide opportunities to participate in outdoor play. However, there are barriers to the provision of outdoor play opportunities at ECECs, such as adverse weather conditions, poorly designed outdoor spaces, outdoor time policies, and early childhood educator comfort levels. Objective: The PROmoting Early Childhood Outside (PRO-ECO) study is a wait-list control cluster randomized trial that evaluates the impact of the PRO-ECO intervention, an innovative outdoor play intervention, on children’s outdoor play behavior. The purpose of this paper was to provide a detailed overview of the pilot study protocol and the methods that will be used to develop, implement, and evaluate the PRO-ECO intervention.publishedVersio