Aplicación de lectoescritura para dispositivos con Sistema Operativo Android

El presente proyecto pretende ser una herramienta para la enseñanza de la lectoescritura (enseñar a leer y a escribir) para niños con discapacidad, haciendo para ello uso de una aplicación que se ejecuta en una tablet con Sistema Operativo (S.O.) Android. Existe un vacío en el mundo de las aplicaciones para tabletas en este campo en el que se intentará poner un grano de arena para, al menos, tener una aplicación que sirva de toma de contacto a los interesados en este campo. Para establecer las funcionalidades más adecuadas al propósito de la herramienta, se ha consultado a profesionales de la logopedia de un colegio de educación especial, con cuya colaboración se ha dado forma a la estructura de la misma. La implementación de la aplicación se ha llevado a cabo con programación en entorno Java para Android. Se han incluido diferentes recursos como imágenes, pictogramas y locuciones tanto elementos con licencia libre, como elementos propios generados ‘ex profeso’ para dar la forma final a la herramienta. Podemos decir que en general esta aplicación puede ser usada para enseña a leer y escribir a cualquier niño, pero se ha dotado de unas ciertas características que la confieren una orientación especial hacia niños con necesidades educativas especiales. Para ello se ha cuidado mucho la estética, para que ésta sea lo más simple y suave posible, para hacer especial hincapié en la atención de los niños y evitar su distracción con elementos visuales innecesarios. Se ha dotado de estímulos visuales y sonoros para fomentar su interés (aplausos en caso de acierto, colores para diferenciar aciertos y errores, etc.). Se han utilizado los tamaños de letra más grandes posibles (para las discapacidades visuales), etc. El mercado cuenta con una ingente cantidad de dispositivos Android, con características muy dispares, de tamaño de pantalla, resolución y versiones del S.O. entre otras. La aplicación se ha desarrollado tratando de dar cobertura al mayor porcentaje de ellos posible. El requisito mínimo de tamaño de pantalla sería de siete pulgadas. Esta herramienta no tiene demasiado sentido en dispositivos con pantallas menores por las características intrínsecas de la misma. No obstante se ha trabajado también en la configuración para dispositivos pequeños, como “smartphones”, no por su valor como herramienta para la enseñanza de la lectoescritura (aunque en algunos casos podría ser viable) sino más bien con fines de prueba y entrenamiento para profesores, padres o tutores que realizarán la labor docente con dispositivos tablet. Otro de los requisitos, como se ha mencionado, para poder ejecutar la aplicación sería la versión mínima de S.O., por debajo de la cual (versiones muy obsoletas) la aplicación sería inviable. Sirva este proyecto pues para cubrir, mediante el uso de la tecnología, un aspecto de la enseñanza con grandes oportunidades de mejora. ----------------------- This Project is aimed to be a tool for teaching reading and writing skills to handicapped children with an Android application. There are no Android applications available on this field, so it is intended to provide at least one option to take contact with. Speech therapy professionals from a special needs school have been asked for the most suitable functions to be included in this tool. The structure of this tool has been made with the cooperation of these professionals. The implementation of the application has been performed through Java coding for Android. Different resources have been included such as pictures, pictograms and sounds, including free licenses resources and self-developed resources. In general, it can be said that this application can be used to teach learning and writing skills to any given kid, however it has been provided of certain features that makes it ideal for children with special educational needs. It has been strongly taken into account the whole aesthetic to be as simple and soft as possible, in order to get attention of children, excluding any visual disturbing elements. It has been provided with sound and visual stimulations, to attract their interest (applauses in cases of correct answers, different colours to differentiate right or wrong answers), etc. There are many different types of Android devices, with very heterogeneous features regarding their screen size, resolution and O.S. version, etc., available today. The application has been developed trying to cover most of them. Minimum screen resolution is seven inches. This tool doesn’t seem to be very useful for smaller screens, for its inner features. Nevertheless, it has been developed for smaller devices as well, like smartphones, not intended to be a tool for teaching reading and writing skills (even it could be possible in some cases), but in a test and training context for teachers, parents or guardians who do the teaching work with tablet devices. Another requirement, as stated before, in order to be able to run the application, it would be the minimum O.S. version, below that (very obsolete versions) the application would become impracticable. Hope this project to be used to fulfill, by means of technology, one area of teaching with great improvement opportunities.

Aplicación de lectoescritura para dispositivos con Sistema Operativo Android

El presente proyecto pretende ser una herramienta para la enseñanza de la lectoescritura (enseñar a leer y a escribir) para niños con discapacidad, haciendo para ello uso de una aplicación que se ejecuta en una tablet con Sistema Operativo (S.O.) Android. Existe un vacío en el mundo de las aplicaciones para tabletas en este campo en el que se intentará poner un grano de arena para, al menos, tener una aplicación que sirva de toma de contacto a los interesados en este campo. Para establecer las funcionalidades más adecuadas al propósito de la herramienta, se ha consultado a profesionales de la logopedia de un colegio de educación especial, con cuya colaboración se ha dado forma a la estructura de la misma. La implementación de la aplicación se ha llevado a cabo con programación en entorno Java para Android. Se han incluido diferentes recursos como imágenes, pictogramas y locuciones tanto elementos con licencia libre, como elementos propios generados ‘ex profeso’ para dar la forma final a la herramienta. Podemos decir que en general esta aplicación puede ser usada para enseña a leer y escribir a cualquier niño, pero se ha dotado de unas ciertas características que la confieren una orientación especial hacia niños con necesidades educativas especiales. Para ello se ha cuidado mucho la estética, para que ésta sea lo más simple y suave posible, para hacer especial hincapié en la atención de los niños y evitar su distracción con elementos visuales innecesarios. Se ha dotado de estímulos visuales y sonoros para fomentar su interés (aplausos en caso de acierto, colores para diferenciar aciertos y errores, etc.). Se han utilizado los tamaños de letra más grandes posibles (para las discapacidades visuales), etc. El mercado cuenta con una ingente cantidad de dispositivos Android, con características muy dispares, de tamaño de pantalla, resolución y versiones del S.O. entre otras. La aplicación se ha desarrollado tratando de dar cobertura al mayor porcentaje de ellos posible. El requisito mínimo de tamaño de pantalla sería de siete pulgadas. Esta herramienta no tiene demasiado sentido en dispositivos con pantallas menores por las características intrínsecas de la misma. No obstante se ha trabajado también en la configuración para dispositivos pequeños, como “smartphones”, no por su valor como herramienta para la enseñanza de la lectoescritura (aunque en algunos casos podría ser viable) sino más bien con fines de prueba y entrenamiento para profesores, padres o tutores que realizarán la labor docente con dispositivos tablet. Otro de los requisitos, como se ha mencionado, para poder ejecutar la aplicación sería la versión mínima de S.O., por debajo de la cual (versiones muy obsoletas) la aplicación sería inviable. Sirva este proyecto pues para cubrir, mediante el uso de la tecnología, un aspecto de la enseñanza con grandes oportunidades de mejora. ABSTRACT. This Project is aimed to be a tool for teaching reading and writing skills to handicapped children with an Android application. There are no Android applications available on this field, so it is intended to provide at least one option to take contact with. Speech therapy professionals from a special needs school have been asked for the most suitable functions to be included in this tool. The structure of this tool has been made with the cooperation of these professionals. The implementation of the application has been performed through Java coding for Android. Different resources have been included such as pictures, pictograms and sounds, including free licenses resources and self-developed resources. In general, it can be said that this application can be used to teach learning and writing skills to any given kid, however it has been provided of certain features that makes it ideal for children with special educational needs. It has been strongly taken into account the whole aesthetic to be as simple and soft as possible, in order to get attention of children, excluding any visual disturbing elements. It has been provided with sound and visual stimulations, to attract their interest (applauses in cases of correct answers, different colours to differentiate right or wrong answers), etc. There are many different types of Android devices, with very heterogeneous features regarding their screen size, resolution and O.S. version, etc., available today. The application has been developed trying to cover most of them. Minimum screen resolution is seven inches. This tool doesn’t seem to be very useful for smaller screens, for its inner features. Nevertheless, it has been developed for smaller devices as well, like smartphones, not intended to be a tool for teaching reading and writing skills (even it could be possible in some cases), but in a test and training context for teachers, parents or guardians who do the teaching work with tablet devices. Another requirement, as stated before, in order to be able to run the application, it would be the minimum O.S. version, below that (very obsolete versions) the application would become impracticable. Hope this project to be used to fulfill, by means of technology, one area of teaching with great improvement opportunities.

Taming transitive permission attack via bytecode rewriting on Android application

Google Android is popular for mobile devices in recent years. The openness and popularity of Android make it a primary target for malware. Even though Android's security mechanisms could defend most malware, its permission model is vulnerable to transitive permission attack, a type of privilege escalation attacks. Many approaches have been proposed to detect this attack by modifying the Android OS. However, the Android's fragmentation problem and requiring rooting Android device hinder those approaches large-scale adoption. In this paper, we present an instrumentation framework, called SEAPP, for Android applications (or “apps”) to detect the transitive permission attack on unmodified Android. SEAPP automatically rewrites an app without requiring its source codes and produces a security-harden app. At runtime, call-chains are built among these apps and detection process is executed before a privileged API is invoked. Our experimental results show that SEAPP could work on a large number of benign apps from the official Android market and malicious apps, with a repackaged success rate of over 99.8%. We also show that our framework effectively tracks call-chains among apps and detects known transitive permission attack with low overhead. Copyright © 2016 John Wiley & Sons, Ltd.

Design and implementation of robust systems for secure malware detection

Malicious software (malware) have significantly increased in terms of number and effectiveness during the past years. Until 2006, such software were mostly used to disrupt network infrastructures or to show coders’ skills. Nowadays, malware constitute a very important source of economical profit, and are very difficult to detect. Thousands of novel variants are released every day, and modern obfuscation techniques are used to ensure that signature-based anti-malware systems are not able to detect such threats. This tendency has also appeared on mobile devices, with Android being the most targeted platform. To counteract this phenomenon, a lot of approaches have been developed by the scientific community that attempt to increase the resilience of anti-malware systems. Most of these approaches rely on machine learning, and have become very popular also in commercial applications. However, attackers are now knowledgeable about these systems, and have started preparing their countermeasures. This has lead to an arms race between attackers and developers. Novel systems are progressively built to tackle the attacks that get more and more sophisticated. For this reason, a necessity grows for the developers to anticipate the attackers’ moves. This means that defense systems should be built proactively, i.e., by introducing some security design principles in their development. The main goal of this work is showing that such proactive approach can be employed on a number of case studies. To do so, I adopted a global methodology that can be divided in two steps. First, understanding what are the vulnerabilities of current state-of-the-art systems (this anticipates the attacker’s moves). Then, developing novel systems that are robust to these attacks, or suggesting research guidelines with which current systems can be improved. This work presents two main case studies, concerning the detection of PDF and Android malware. The idea is showing that a proactive approach can be applied both on the X86 and mobile world. The contributions provided on this two case studies are multifolded. With respect to PDF files, I first develop novel attacks that can empirically and optimally evade current state-of-the-art detectors. Then, I propose possible solutions with which it is possible to increase the robustness of such detectors against known and novel attacks. With respect to the Android case study, I first show how current signature-based tools and academically developed systems are weak against empirical obfuscation attacks, which can be easily employed without particular knowledge of the targeted systems. Then, I examine a possible strategy to build a machine learning detector that is robust against both empirical obfuscation and optimal attacks. Finally, I will show how proactive approaches can be also employed to develop systems that are not aimed at detecting malware, such as mobile fingerprinting systems. In particular, I propose a methodology to build a powerful mobile fingerprinting system, and examine possible attacks with which users might be able to evade it, thus preserving their privacy. To provide the aforementioned contributions, I co-developed (with the cooperation of the researchers at PRALab and Ruhr-Universität Bochum) various systems: a library to perform optimal attacks against machine learning systems (AdversariaLib), a framework for automatically obfuscating Android applications, a system to the robust detection of Javascript malware inside PDF files (LuxOR), a robust machine learning system to the detection of Android malware, and a system to fingerprint mobile devices. I also contributed to develop Android PRAGuard, a dataset containing a lot of empirical obfuscation attacks against the Android platform. Finally, I entirely developed Slayer NEO, an evolution of a previous system to the detection of PDF malware. The results attained by using the aforementioned tools show that it is possible to proactively build systems that predict possible evasion attacks. This suggests that a proactive approach is crucial to build systems that provide concrete security against general and evasion attacks.

Towards an understanding of the impact of advertising on data leaks

Recent investigations have determined that many Android applications in both official and non-official online markets expose details of the user's mobile phone without user consent. In this paper, for the first time in the research literature, we provide a full investigation of why such applications leak, how they leak and where the data is leaked to. In order to achieve this, we employ a combination of static and dynamic analysis based on examination of Java classes and application behaviour for a data set of 123 samples, all pre-determined as being free from malicious software. Despite the fact that anti-virus vendor software did not flag any of these samples as malware, approximately 10% of them are shown to leak data about the mobile phone to a third-party; applications from the official market appear to be just as susceptible to such leaks as applications from the non-official markets.

Detección de marcas para realidad aumentada sobre dispositivos móviles

Este documento es una guía para el desarrollo de una aplicación para dispositivos móviles en Android. Dicha aplicación combina las técnicas de visión por computador para calibrar la cámara del dispositivo y localizar un elemento en el espacio en base a esos los parámetros calculados en la calibración. El diseño de la aplicación incluye las decisiones sobre la forma en que se reciben los inputs de la aplicación, que patrones se utilizan en la calibración y en la localización y como se muestran los resultados finales al usuario. También incluye un diagrama de flujo de información que representa el tránsito de esta entre los diferentes módulos. La implementación comienza con la configuración de un entorno para desarrollar aplicaciones con parte nativa en Android, después comenta el código de la aplicación paso por paso incluyendo comentarios sobre los archivos adicionales necesarios para la compilación y finalmente detalla los archivos dedicados a la interfaz. Los experimentos incluyen una breve descripción sobre cómo interpretar los resultados seguidos de una serie de imágenes tomadas de la aplicación con diferentes localizaciones del patrón. En la entrega se incluye también un video. En el capítulo de resultados y conclusiones podemos encontrar observaciones sobre el desarrollo de la práctica, opiniones sobre su utilidad, y posibles mejoras.---ABSTRACT---This document is a guide that describes the development of and application for mobile devices in Android OS. The application combines computer vision techniques to calibrate the device camera and locate an element in the real world based on the parameters of the calibration The design of the application includes the decisions over the way that the application receives its input data, the patterns used in the calibration and localization and how the results are shown to the user. It also includes a flow chart that describes how the information travels along the application modules. The development begins with the steps necessary to configure the environment to develop native Android applications, then it explains the code step by step, including commentaries on the additional files necessary to build the application and details the files of the user interface. The experiments chapter explains the way the results are shown in the experiments before showing samples of different pattern localizations. There is also a video attached. In the conclusions chapter we can find observations on the development of the TFG, opinions about its usefulness, and possibilities of improvement in the future.

Aplicação para pesquisa de clip arts em dispositivos móveis baseada em esboços

Trabalho Final de Mestrado para obtenção do Grau de Mestre em Engenharia de Redes de Comunicação e Multimédia

Developing and benchmarking native Linux applications on Android

Smartphones get increasingly popular where more and more smartphone platforms emerge. Special attention was gained by the open source platform Android which was presented by the Open Handset Alliance (OHA) hosting members like Google, Motorola, and HTC. Android uses a Linux kernel and a stripped-down userland with a custom Java VM set on top. The resulting system joins the advantages of both environments, while third-parties are intended to develop only Java applications at the moment. In this work, we present the benefit of using native applications in Android. Android includes a fully functional Linux, and using it for heavy computational tasks when developing applications can bring in substantional performance increase. We present how to develop native applications and software components, as well as how to let Linux applications and components communicate with Java programs. Additionally, we present performance measurements of native and Java applications executing identical tasks. The results show that native C applications can be up to 30 times as fast as an identical algorithm running in Dalvik VM. Java applications can become a speed-up of up to 10 times if utilizing JNI.

A Comparison of Geo-tagging in Mobile Internet Browsing Applications on iOS and Android.

Nowadays there is almost no crime committed without a trace of digital evidence, and since the advanced functionality of mobile devices today can be exploited to assist in crime, the need for mobile forensics is imperative. Many of the mobile applications available today, including internet browsers, will request the user’s permission to access their current location when in use. This geolocation data is subsequently stored and managed by that application's underlying database files. If recovered from a device during a forensic investigation, such GPS evidence and track points could hold major evidentiary value for a case. The aim of this paper is to examine and compare to what extent geolocation data is available from the iOS and Android operating systems. We focus particularly on geolocation data recovered from internet browsing applications, comparing the native Safari and Browser apps with Google Chrome, downloaded on to both platforms. All browsers were used over a period of several days at various locations to generate comparable test data for analysis. Results show considerable differences not only in the storage locations and formats, but also in the amount of geolocation data stored by different browsers and on different operating systems.

Enhancing security of linux-based android devices

Our daily lives become more and more dependent upon smartphones due to their increased capabilities. Smartphones are used in various ways from payment systems to assisting the lives of elderly or disabled people. Security threats for these devices become increasingly dangerous since there is still a lack of proper security tools for protection. Android emerges as an open smartphone platform which allows modification even on operating system level. Therefore, third-party developers have the opportunity to develop kernel-based low-level security tools which is not normal for smartphone platforms. Android quickly gained its popularity among smartphone developers and even beyond since it bases on Java on top of "open" Linux in comparison to former proprietary platforms which have very restrictive SDKs and corresponding APIs. Symbian OS for example, holding the greatest market share among all smartphone OSs, was closing critical APIs to common developers and introduced application certification. This was done since this OS was the main target for smartphone malwares in the past. In fact, more than 290 malwares designed for Symbian OS appeared from July 2004 to July 2008. Android, in turn, promises to be completely open source. Together with the Linux-based smartphone OS OpenMoko, open smartphone platforms may attract malware writers for creating malicious applications endangering the critical smartphone applications and owners� privacy. In this work, we present our current results in analyzing the security of Android smartphones with a focus on its Linux side. Our results are not limited to Android, they are also applicable to Linux-based smartphones such as OpenMoko Neo FreeRunner. Our contribution in this work is three-fold. First, we analyze android framework and the Linux-kernel to check security functionalities. We survey wellaccepted security mechanisms and tools which can increase device security. We provide descriptions on how to adopt these security tools on Android kernel, and provide their overhead analysis in terms of resource usage. As open smartphones are released and may increase their market share similar to Symbian, they may attract attention of malware writers. Therefore, our second contribution focuses on malware detection techniques at the kernel level. We test applicability of existing signature and intrusion detection methods in Android environment. We focus on monitoring events on the kernel; that is, identifying critical kernel, log file, file system and network activity events, and devising efficient mechanisms to monitor them in a resource limited environment. Our third contribution involves initial results of our malware detection mechanism basing on static function call analysis. We identified approximately 105 Executable and Linking Format (ELF) executables installed to the Linux side of Android. We perform a statistical analysis on the function calls used by these applications. The results of the analysis can be compared to newly installed applications for detecting significant differences. Additionally, certain function calls indicate malicious activity. Therefore, we present a simple decision tree for deciding the suspiciousness of the corresponding application. Our results present a first step towards detecting malicious applications on Android-based devices.

Static analysis of executables for collaborative malware detection on Android

Smartphones are getting increasingly popular and several malwares appeared targeting these devices. General countermeasures to smartphone malwares are currently limited to signature-based antivirus scanners which efficiently detect known malwares, but they have serious shortcomings with new and unknown malwares creating a window of opportunity for attackers. As smartphones become host for sensitive data and applications, extended malware detection mechanisms are necessary complying with the corresponding resource constraints. The contribution of this paper is twofold. First, we perform static analysis on the executables to extract their function calls in Android environment using the command readelf. Function call lists are compared with malware executables for classifying them with PART, Prism and Nearest Neighbor Algorithms. Second, we present a collaborative malware detection approach to extend these results. Corresponding simulation results are presented.

Smartphone malware evolution revisited : Android next target?

Smartphones started being targets for malware in June 2004 while malware count increased steadily until the introduction of a mandatory application signing mechanism for Symbian OS in 2006. From this point on, only few news could be read on this topic. Even despite of new emerging smartphone platforms, e.g. android and iPhone, malware writers seemed to lose interest in writing malware for smartphones giving users an unappropriate feeling of safety. In this paper, we revisit smartphone malware evolution for completing the appearance list until end of 2008. For contributing to smartphone malware research, we continue this list by adding descriptions on possible techniques for creating the first malware(s) for Android platform. Our approach involves usage of undocumented Android functions enabling us to execute native Linux application even on retail Android devices. This can be exploited to create malicious Linux applications and daemons using various methods to attack a device. In this manner, we also show that it is possible to bypass the Android permission system by using native Linux applications.

An Android application sandbox system for suspicious software detection

Smartphones are steadily gaining popularity, creating new application areas as their capabilities increase in terms of computational power, sensors and communication. Emerging new features of mobile devices give opportunity to new threats. Android is one of the newer operating systems targeting smartphones. While being based on a Linux kernel, Android has unique properties and specific limitations due to its mobile nature. This makes it harder to detect and react upon malware attacks if using conventional techniques. In this paper, we propose an Android Application Sandbox (AASandbox) which is able to perform both static and dynamic analysis on Android programs to automatically detect suspicious applications. Static analysis scans the software for malicious patterns without installing it. Dynamic analysis executes the application in a fully isolated environment, i.e. sandbox, which intervenes and logs low-level interactions with the system for further analysis. Both the sandbox and the detection algorithms can be deployed in the cloud, providing a fast and distributed detection of suspicious software in a mobile software store akin to Google's Android Market. Additionally, AASandbox might be used to improve the efficiency of classical anti-virus applications available for the Android operating system.

Monitoring android for collaborative anomaly detection : a first architectural draft

Our daily lives become more and more dependent upon smartphones due to their increased capabilities. Smartphones are used in various ways, e.g. for payment systems or assisting the lives of elderly or disabled people. Security threats for these devices become more and more dangerous since there is still a lack of proper security tools for protection. Android emerges as an open smartphone platform which allows modification even on operating system level and where third-party developers first time have the opportunity to develop kernel-based low-level security tools. Android quickly gained its popularity among smartphone developers and even beyond since it bases on Java on top of "open" Linux in comparison to former proprietary platforms which have very restrictive SDKs and corresponding APIs. Symbian OS, holding the greatest market share among all smartphone OSs, was even closing critical APIs to common developers and introduced application certification. This was done since this OS was the main target for smartphone malwares in the past. In fact, more than 290 malwares designed for Symbian OS appeared from July 2004 to July 2008. Android, in turn, promises to be completely open source. Together with the Linux-based smartphone OS OpenMoko, open smartphone platforms may attract malware writers for creating malicious applications endangering the critical smartphone applications and owners privacy. Since signature-based approaches mainly detect known malwares, anomaly-based approaches can be a valuable addition to these systems. They base on mathematical algorithms processing data that describe the state of a certain device. For gaining this data, a monitoring client is needed that has to extract usable information (features) from the monitored system. Our approach follows a dual system for analyzing these features. On the one hand, functionality for on-device light-weight detection is provided. But since most algorithms are resource exhaustive, remote feature analysis is provided on the other hand. Having this dual system enables event-based detection that can react to the current detection need. In our ongoing research we aim to investigates the feasibility of light-weight on-device detection for certain occasions. On other occasions, whenever significant changes are detected on the device, the system can trigger remote detection with heavy-weight algorithms for better detection results. In the absence of the server respectively as a supplementary approach, we also consider a collaborative scenario. Here, mobile devices sharing a common objective are enabled by a collaboration module to share information, such as intrusion detection data and results. This is based on an ad-hoc network mode that can be provided by a WiFi or Bluetooth adapter nearly every smartphone possesses.