Acceleration of an unstructured hybrid mesh RANS solver by porting to GPU architectures

The modern CFD process consists of mesh generation, flow solving and post-processing integrated into an automated workflow. During the last several years we have developed and published research aimed at producing a meshing and geometry editing system, implemented in an end-to-end parallel, scalable manner and capable of automatic handling of large scale, real world applications. The particular focus of this paper is the associated unstructured mesh RANS flow solver and the porting of it to GPU architectures. After briefly describing the solver itself, the special issues associated with porting codes using unstructured data structures are discussed - followed by some application examples. Copyright © 2011 by W.N. Dawes.

基于JUPITER的uClinux移植及其应用实例；uClinux Porting Based on JUPITER and example of Application

介绍了嵌入式处理器JUPITER,详细分析了uClinux嵌入式操作系统的特点。给出了基于JUPITER和uClinux的嵌入式网关。通过嵌入式网关实例详细说明了在JUPITER上的移植uClinux的方法,以及uClinux下应用程序的开发。

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.

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

Implementação de plataforma web e android no âmbito da associação Anddvis

Trabalho de Projeto apresentado ao Instituto Superior de Contabilidade e Administração do Porto para a obtenção do grau de Mestre em Marketing Digital, sob orientação do Mestre Paulo Gonçalves

Sistema de localização de veículos para smartphone android

Atualmente os sistemas Automatic Vehicle Location (AVL) fazem parte do dia-a-dia de muitas empresas. Esta tecnologia tem evoluído significativamente ao longo da última década, tornando-se mais acessível e fácil de utilizar. Este trabalho consiste no desenvolvimento de um sistema de localização de veículos para smartphone Android. Para tal, foram desenvolvidas duas aplicações: uma aplicação de localização para smarphone Android e uma aplicação WEB de monitorização. A aplicação de localização permite a recolha de dados de localização GPS e estabelecer uma rede piconet Bluetooth, admitindo assim a comunicação simultânea com a unidade de controlo de um veículo (ECU) através de um adaptador OBDII/Bluetooth e com até sete sensores/dispositivos Bluetooth que podem ser instalados no veículo. Os dados recolhidos pela aplicação Android são enviados periodicamente (intervalo de tempo definido pelo utilizador) para um servidor Web No que diz respeito à aplicação WEB desenvolvida, esta permite a um gestor de frota efetuar a monitorização dos veículos em circulação/registados no sistema, podendo visualizar a posição geográfica dos mesmos num mapa interativo (Google Maps), dados do veículo (OBDII) e sensores/dispositivos Bluetooth para cada localização enviada pela aplicação Android. O sistema desenvolvido funciona tal como esperado. A aplicação Android foi testada inúmeras vezes e a diferentes velocidades do veículo, podendo inclusive funcionar em dois modos distintos: data logger e data pusher, consoante o estado da ligação à Internet do smartphone. Os sistemas de localização baseados em smartphone possuem vantagens relativamente aos sistemas convencionais, nomeadamente a portabilidade, facilidade de instalação e baixo custo.

Rapps Sistema de recomendação de apps de produtividade para Android

Nos últimos anos, a nossa sociedade sofreu alterações significativas ao nível tecnológico que têm vindo a modificar o quotidiano do cidadão e transportaram para a palma da mão um conjunto significativo de tarefas até há poucos anos impensáveis. Atualmente, torna-se possível realizar as mais simples tarefas como, a título de exemplo, efetuar um cálculo matemático, tirar fotografias ou registar numa agenda um compromisso, ou tarefas mais complexas, como por exemplo, escrever ou editar um documento, trabalhar numa folha de cálculo ou enviar um e-mail com um anexo, isto tudo com o recurso a um simples dispositivo móvel, conhecido como smartphone ou tablet. Apesar de existirem diversos tipos de apps que seriam um bom auxílio para o aumento da produtividade dos utilizadores de dispositivos móveis Android, nem todos têm conhecimento das mesmas, pelo que é importante que os utilizadores tenham conhecimentos das vantagens da utilização destes recursos e de tudo o que podem realizar com os seus dispositivos com o objetivo de aumentar a sua produtividade profissional ou pessoal. O presente estudo pretende contribuir para uma análise sobre a potencial utilização das novas tecnologias, mais propriamente estudando e recomendando apps de produtividade. Com este intuito foi criada uma app de recomendação de aplicações de produtividade com recurso a um método de sistemas de recomendação. São apresentados os resultados e as conclusões, com recurso a opiniões de potenciais utilizadores.