D-Lappse: time-lapseak egiteko dolly bat automatizatzeko Android aplikazioa

Jakina da informatika oso gai zabala dela. Horregatik, oro har, sistema informatikoen garatzaileak sistemaren osagai bakan batzuetaz soilik arduratzen dira. Proiektu honek hardwarearen eta softwarearen munduak uztartzea du helburu; hardware- eta softwareosagaiak dituen sistema bat sortu, behar diren osagai guztiak garatuz. Horretaz gain, garatutakoa erabilgarria izatea bilatu da, hau da, funtzio praktiko eta erreal bat edukitzea. Horretarako, Android eta Arduino plataformak aztertu dira: Android erabiltzaileari interfaze grafikoa eskaini eta elkarrekintza burutzeko erabili da; Arduino, berriz, hardwarearen kontrolatzailea izateko. Horrekin, eragingailuak kontrolatuz, time-lapseak egiteko sistema automatizatua garatu da; D-Lappse Android aplikazioarekin sistema kontrolatu daiteke eta dollyari aginduak bidali, argazki-sekuentziak era automatizatu batean egiteko.

Centro de gestión de notificaciones Push para dispositivos móviles basados en IOS y Android

En esta memoria se presenta el proyecto de fin de carrera “Centro de gestión de notificaciones Push para dispositivos móviles basados en IOS y Android”, cuyo objetivo es crear una herramienta web, de libre distribución, que permita gestionar, programar y realizar envíos de notificaciones Push para aplicaciones móviles que funcionen bajo entornos IOS (Apple) y Android.

JonBike. Aplicación Android para bicicletas basada en geolocalización

En este Proyecto Fin de Carrera se ha realizado el diseño y la implementación de la aplicación JonBike. Tiene como objetivo mejorar y optimizar el seguimiento de los entrenamientos de los ciclistas. El desarrollo se ha realizado en un marco de integración y colaboración directa de los usuarios en el proyecto, partiendo de un Producto Mínimo Viable o "Minimum Viable Product" (MVP) inicial e integrando su feedback en la progresiva ampliación de las características del servicio. El sistema está compuesto por una aplicación móvil nativa para Android y una fuente de datos alojada en un servidor remoto. En implementación se han utilizado tecnologías emergentes, todas de código abierto: MongoDB junto con MongoLab para el almacenamiento de datos y plataforma base para el backend, y desarrollando como cliente, una aplicación Android nativa.

Android-entzako SmartWatch prototipo baten garapena

GAL honek Android-entzako SmartWatch baten prototipo txiki bat egitea du helburu. SmartWatch-aren egiturari dagokionez, ondorengo atalak aurreikusten dira: • Eskumuturreko erlojua. Telefonotik datozen jakinarazpenak bere pantailatxoan adieraziko ditu. • Telefono mugikorra. Android sistema eragilean oinarrituta, hari gabeko teknologia bitartez erlojuari datuak bidaliko dizkion aplikazio bat garatuko da.

Desarrollo de cliente OMA PoC para Android

[ES]Se presenta el siguiente proyecto de desarrollo, enfocado en el contexto de suplir las necesidades de los equipos de emergencias. Actualmente los equipos de emergencias, hacen uso de walkie talkies para comunicarse entre sí, es decir, hacen uso de la tecnología PTT (Push-to-talk). Es una comunicación unidireccional de un usuario a muchos, de manera que pueden transmitir la información necesaria a varios usuarios simultáneamente. Por otra parte, la comunicación mediante esta tecnología hace posible que sea en tiempo real sin prácticamente ningún retraso. Sin embargo el uso de esta forma de comunicación trae consigo algunos inconvenientes. Por ejemplo, los dispositivos tienen un rango de alcance de transmisión-recepción limitado. Además la banda de frecuencia para poder comunicarse debe ser reservada a fin de evitar posibles problemas de comunicación debido a que el canal estuviera ocupado por otros usuarios, posibles interferencias, etc. La reserva de este rango de frecuencia, supone un coste añadido para el uso de esta tecnología. Con el desarrollo de las redes móviles que ofrecen grandes velocidades de transmisión, se hace posible el uso de la tecnología PTT mediante la red móvil existente. De esta manera, surge el siguiente proyecto de desarrollo de una aplicación móvil Android, como cliente que haga uso de la tecnología PTT sobre la red móvil PoC (Push-to-talk Over Cellular).

Analysis of Bayesian classification-based approaches for Android malware detection

Mobile malware has been growing in scale and complexity spurred by the unabated uptake of smartphones worldwide. Android is fast becoming the most popular mobile platform resulting in sharp increase in malware targeting the platform. Additionally, Android malware is evolving rapidly to evade detection by traditional signature-based scanning. Despite current detection measures in place, timely discovery of new malware is still a critical issue. This calls for novel approaches to mitigate the growing threat of zero-day Android malware. Hence, the authors develop and analyse proactive machine-learning approaches based on Bayesian classification aimed at uncovering unknown Android malware via static analysis. The study, which is based on a large malware sample set of majority of the existing families, demonstrates detection capabilities with high accuracy. Empirical results and comparative analysis are presented offering useful insight towards development of effective static-analytic Bayesian classification-based solutions for detecting unknown Android malware.

Android Malware Detection Using Parallel Machine Learning Classifiers

Mobile malware has continued to grow at an alarming rate despite on-going mitigation efforts. This has been much more prevalent on Android due to being an open platform that is rapidly overtaking other competing platforms in the mobile smart devices market. Recently, a new generation of Android malware families has emerged with advanced evasion capabilities which make them much more difficult to detect using conventional methods. This paper proposes and investigates a parallel machine learning based classification approach for early detection of Android malware. Using real malware samples and benign applications, a composite classification model is developed from parallel combination of heterogeneous classifiers. The empirical evaluation of the model under different combination schemes demonstrates its efficacy and potential to improve detection accuracy. More importantly, by utilizing several classifiers with diverse characteristics, their strengths can be harnessed not only for enhanced Android malware detection but also quicker white box analysis by means of the more interpretable constituent classifiers.

A New Android Malware Detection Method Using Bayesian Classification

Mobile malware has been growing in scale and complexity as smartphone usage continues to rise. Android has surpassed other mobile platforms as the most popular whilst also witnessing a dramatic increase in malware targeting the platform. A worrying trend that is emerging is the increasing sophistication of Android malware to evade detection by traditional signature-based scanners. As such, Android app marketplaces remain at risk of hosting malicious apps that could evade detection before being downloaded by unsuspecting users. Hence, in this paper we present an effective approach to alleviate this problem based on Bayesian classification models obtained from static code analysis. The models are built from a collection of code and app characteristics that provide indicators of potential malicious activities. The models are evaluated with real malware samples in the wild and results of experiments are presented to demonstrate the effectiveness of the proposed approach.

High accuracy android malware detection using ensemble learning

With over 50 billion downloads and more than 1.3 million apps in Google’s official market, Android has continued to gain popularity amongst smartphone users worldwide. At the same time there has been a rise in malware targeting the platform, with more recent strains employing highly sophisticated detection avoidance techniques. As traditional signature based methods become less potent in detecting unknown malware, alternatives are needed for timely zero-day discovery. Thus this paper proposes an approach that utilizes ensemble learning for Android malware detection. It combines advantages of static analysis with the efficiency and performance of ensemble machine learning to improve Android malware detection accuracy. The machine learning models are built using a large repository of malware samples and benign apps from a leading antivirus vendor. Experimental results and analysis presented shows that the proposed method which uses a large feature space to leverage the power of ensemble learning is capable of 97.3 % to 99% detection accuracy with very low false positive rates.

Android Malware Detection: an Eigenspace Analysis Approach

The battle to mitigate Android malware has become more critical with the emergence of new strains incorporating increasingly sophisticated evasion techniques, in turn necessitating more advanced detection capabilities. Hence, in this paper we propose and evaluate a machine learning based approach based on eigenspace analysis for Android malware detection using features derived from static analysis characterization of Android applications. Empirical evaluation with a dataset of real malware and benign samples show that detection rate of over 96% with a very low false positive rate is achievable using the proposed method.

Android as a cloud ticket validator

Trabalho Final de Mestrado para a obtenção do grau de Mestre em Engenharia Informática e de Computadores

Simulação de plataformas para dispositivos móveis: infraestrutura de simulação sensorial para android

Projeto para obtenção do grau de Mestre em Engenharia Informática e de Computadores