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.

Coupled Effects of a Perturbation in a Complex Structure Observed with SDO/AIA, SDO/HMI and ROSA/HARDcam

We study properties of intensity fluctuations in NOAA Active Region 11250 observed on 13 July 2011 starting at UT 13:32. Included are data obtained in the EUV bands of the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory (SDO/AIA) as well as nearly simultaneous observations of the chromosphere made, at much higher spatial and temporal resolution, with the Rapid Oscillations in the Solar Atmosphere (ROSA) and Hydrogen-Alpha Rapid Dynamics camera (HARDcam) systems at the Dunn Solar Telescope. A complex structure seen in both the ROSA/HARDcam and SDO data sets comprises a system of loops extending outward from near the boundary of the leading sunspot umbra. It is visible in the ROSA Ca II K and HARDcam Hα images, as well as the SDO 304 Å, 171 Å and 193 Å channels, and it thus couples the chromosphere, transition region and corona. In the ground-based images the loop structure is 4.1 Mm long. Some 17.5 Mm, can be traced in the SDO/AIA data. The chromospheric emissions observed by ROSA and HARDcam appear to occupy the inner, and apparently cooler and lower, quarter of the loop. We compare the intensity fluctuations of two points within the structure. From alignment with SDO/HMI images we identify a point "A" near the loop structure, which sits directly above a bipolar magnetic feature in the photosphere. Point "B" is characteristic of locations within the loops that are visible in both the ROSA/HARDcam and the SDO/AIA data. The intensity traces for point A are quiet during the first part of the data string. At time ~ 19 min they suddenly begin a series of impulsive brightenings. In the 171 Å and 193 Å coronal lines the brightenings are localized impulses in time, but in the transition region line at 304 Å they are more extended in time. The intensity traces in the 304 Å line for point B shows a quasi-periodic signal that changes properties at about 19 min. The wavelet power spectra are characterized by two periodicities. A 6.7 min period extends from the beginning of the series until about 25 minutes, and another signal with period ~3 min starts at about 20 min. The 193 Å power spectrum has a characteristic period of 5 min, before the 20 min transition and a 2.5 min periodicity afterward. In the case of HARDcam Hα data a localized 4 min periodicity can be found until about 7 min, followed by a quiet regime. After ~20 min a 2.3 min periodicity appears. Interestingly a coronal loop visible in the 94 Å line that is centrally located in the AR, running from the leading umbra to the following polarity, at about time 20 min undergoes a strong brightening beginning at the same moment all along 15 Mm of its length. The fact that these different signals all experience a clear-cut change at time about 20 min suggests an underlying organizing mechanism. Given that point A has a direct connection to the photospheric magnetic bipole, we conjecture that the whole extended structure is connected in a complex manner to the underlying magnetic field. The periodicities in these features may favor the wave nature rather than upflows and interpretations will be discussed.

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.

From Tin Toy to Android

This presentation is intended to show the use of Adobe Presenter to create a narrated presentation, and can be compared with the version recorded using Camtasia Studio.

From Tin Toy to Android

This presentation is intended to show the use of Camtasia Studio to create a narrated presentation, and can be compared with the version recorded using Adobe Presenter.