Automatically Discovering, Reporting and Reproducing Android Application Crashes

Mobile developers face unique challenges when detecting and reporting crashes in apps due to their prevailing GUI event-driven nature and additional sources of inputs (e.g., sensor readings). To support developers in these tasks, we introduce a novel, automated approach called CRASHSCOPE. This tool explores a given Android app using systematic input generation, according to several strategies informed by static and dynamic analyses, with the intrinsic goal of triggering crashes. When a crash is detected, CRASHSCOPE generates an augmented crash report containing screenshots, detailed crash reproduction steps, the captured exception stack trace, and a fully replayable script that automatically reproduces the crash on a target device(s). We evaluated CRASHSCOPE's effectiveness in discovering crashes as compared to five state-of-the-art Android input generation tools on 61 applications. The results demonstrate that CRASHSCOPE performs about as well as current tools for detecting crashes and provides more detailed fault information. Additionally, in a study analyzing eight real-world Android app crashes, we found that CRASHSCOPE's reports are easily readable and allow for reliable reproduction of crashes by presenting more explicit information than human written reports.Comment: 12 pages, in Proceedings of 9th IEEE International Conference on Software Testing, Verification and Validation (ICST'16), Chicago, IL, April 10-15, 2016, pp. 33-4

Emulation vs Instrumentation for Android Malware Detection

In resource constrained devices, malware detection is typically based on offline analysis using emulation. In previous work it has been claimed that such emulation fails for a significant percentage of Android malware because well-designed malware detects that the code is being emulated. An alternative to emulation is malware analysis based on code that is executing on an actual Android device. In this research, we collect features from a corpus of Android malware using both emulation and on-phone instrumentation. We train machine learning models based on emulated features and also train models based on features collected via instrumentation, and we compare the results obtained in these two cases

Boosting Static Analysis of Android Apps through Code Instrumentation

Static analysis has been applied to dissect Android apps for many years. The main advantage of using static analysis is its efficiency and entire code coverage characteristics. However, the community has not yet produced complete tools to perform in-depth static analysis, putting users at risk to malicious apps. Because of the diverse challenges caused by Android apps, it is hard for a single tool to efficiently address all of them. Thus, in this work, we propose to boost static analysis of Android apps through code instrumentation, in which the knotty code can be reduced or simplified into an equivalent but analyzable code. Consequently, existing static analyzers, without any modification, can be leveraged to perform extensive analysis, although originally they cannot. Previously, we have successfully applied instrumentation for two challenges of static analysis of Android apps: Inter-Component Communication (ICC) and Reflection. However, these two case studies are implemented separately and the implementation is not reusable, letting some functionality, that could be reused from one to another, be reinvented and thus lots of resources are wasted. To this end, in this work, we aim at providing a generic and non-invasive approach for existing static analyzers, enabling them to perform more broad analysis

Dynamic behavior analysis of android applications for malware detection

Android is most popular operating system for smartphones and small devices with 86.6% market share (Chau 2016). Its open source nature makes it more prone to attacks creating a need for malware analysis. Main approaches for detecting malware intents of mobile applications are based on either static analysis or dynamic analysis. In static analysis, apps are inspected for suspicious patterns of code to identify malicious segments. However, several obfuscation techniques are available to provide a guard against such analysis. The dynamic analysis on the other hand is a behavior-based detection method that involves investigating the run-time behavior of the suspicious app to uncover malware. The present study extracts the system call behavior of 216 malicious apps and 278 normal apps to construct a feature vector for training a classifier. Seven data classification techniques including decision tree, random forest, gradient boosting trees, k-NN, Artificial Neural Network, Support Vector Machine and deep learning were applied on this dataset. Three feature ranking techniques were usedto select appropriate features from the set of 337 attributes (system calls). These techniques of feature ranking included information gain, Chi-square statistic and correlation analysis by determining weights of the features. After discarding select features with low ranks the performances of the classifiers were measured using accuracy and recall. Experiments show that Support Vector Machines (SVM) after selecting features through correlation analysis outperformed other techniques where an accuracy of 97.16% is achieved with recall 99.54% (for malicious apps). The study also contributes by identifying the set of systems calls that are crucial in identifying malicious intent of android apps

Android source code vulnerability detection: a systematic literature review

The use of mobile devices is rising daily in this technological era. A continuous and increasing number of mobile applications are constantly offered on mobile marketplaces to fulfil the needs of smartphone users. Many Android applications do not address the security aspects appropriately. This is often due to a lack of automated mechanisms to identify, test, and fix source code vulnerabilities at the early stages of design and development. Therefore, the need to fix such issues at the initial stages rather than providing updates and patches to the published applications is widely recognized. Researchers have proposed several methods to improve the security of applications by detecting source code vulnerabilities and malicious codes. This Systematic Literature Review (SLR) focuses on Android application analysis and source code vulnerability detection methods and tools by critically evaluating 118 carefully selected technical studies published between 2016 and 2022. It highlights the advantages, disadvantages, applicability of the proposed techniques and potential improvements of those studies. Both Machine Learning (ML) based methods and conventional methods related to vulnerability detection are discussed while focusing more on ML-based methods since many recent studies conducted experiments with ML. Therefore, this paper aims to enable researchers to acquire in-depth knowledge in secure mobile application development while minimizing the vulnerabilities by applying ML methods. Furthermore, researchers can use the discussions and findings of this SLR to identify potential future research and development directions

DroidRA: Taming Reflection to Support Whole-Program Analysis of Android Apps

Android developers heavily use reflection in their apps for legitimate reasons, but also significantly for hiding malicious actions. Unfortunately, current state-of-the-art static analysis tools for Android are challenged by the presence of reflective calls which they usually ignore. Thus, the results of their security analysis, e.g., for private data leaks, are inconsistent given the measures taken by malware writers to elude static detection. We propose the DroidRA instrumentation-based approach to address this issue in a non-invasive way. With DroidRA, we reduce the resolution of reflective calls to a composite constant propagation problem. We leverage the COAL solver to infer the values of reflection targets and app, and we eventually instrument this app to include the corresponding traditional Java call for each reflective call. Our approach allows to boost an app so that it can be immediately analyzable, including by such static analyzers that were not reflection-aware. We evaluate DroidRA on benchmark apps as well as on real-world apps, and demonstrate that it can allow state-of-the-art tools to provide more sound and complete analysis results

Reflection-Aware Static Analysis of Android Apps

We demonstrate the benefits of DroidRA, a tool for taming reflection in Android apps. DroidRA first statically extracts reflection-related object values from a given Android app. Then, it leverages the extracted values to boost the app in a way that reflective calls are no longer a challenge for existing static analyzers. This is achieved through a bytecode instrumentation approach, where reflective calls are supplemented with explicit traditional Java method calls which can be followed by state-of-the-art analyzers which do not handle reflection. Instrumented apps can thus be completely analyzed by existing static analyzers, which are no longer required to be modified to support reflection-aware analysis. The video demo of DroidRA can be found at https://youtu.be/-HW0V68aAW

Mining AndroZoo: A Retrospect

This paper presents a retrospect of an Android app collection named AndroZoo and some research works conducted on top of the collection. AndroZoo is a growing collection of Android apps from various markets including the official Google Play. At the moment, over five million Android apps have been collected. Based on AndroZoo, we have explored several directions that mine Android apps for resolving various challenges. In this work, we summarize those resolved mining challenges in three research dimensions, including code analysis, app evolution analysis, malware analysis, and present in each dimension several case studies that experimentally demonstrate the usefulness of AndroZoo

Malware detection based on dynamic analysis features

The widespread usage of mobile devices and their seamless adaptation to each users' needs by the means of useful applications (Apps), makes them a prime target for malware developers to get access to sensitive user data, such as banking details, or to hold data hostage and block user access. These apps are distributed in marketplaces that host millions and therefore have their own forms of automated malware detection in place in order to deter malware developers and keep their app store (and reputation) trustworthy, but there are still a number of apps that are able to bypass these detectors and remain available in the marketplace for any user to download. Current malware detection strategies rely mostly on using features extracted statically, dynamically or a conjunction of both, and making them suitable for machine learning applications, in order to scale detection to cover the number of apps that are submited to the marketplace. In this article, the main focus is the study of the effectiveness of these automated malware detection methods and their ability to keep up with the proliferation of new malware and its ever-shifting trends. By analising the performance of ML algorithms trained, with real world data, on diferent time periods and time scales with features extracted statically, dynamically and from user-feedback, we are able to identify the optimal setup to maximise malware detection.O uso generalizado de dispositivos móveis e sua adaptação perfeita às necessidades de cada utilizador por meio de aplicativos úteis (Apps) tornam-os um alvo principal para que criadores de malware obtenham acesso a dados confidenciais do usuário, como detalhes bancários, ou para reter dados e bloquear o acesso do utilizador. Estas apps são distribuídas em mercados que alojam milhões, e portanto, têm as suas próprias formas de detecção automatizada de malware, a fim de dissuadir os desenvolvedores de malware e manter sua loja de apps (e reputação) confiável, mas ainda existem várias apps capazes de ignorar esses detectores e permanecerem disponíveis no mercado para qualquer utilizador fazer o download. As estratégias atuais de detecção de malware dependem principalmente do uso de recursos extraídos estaticamente, dinamicamente ou de uma conjunção de ambos, e de torná-los adequados para aplicações de aprendizagem automática, a fim de dimensionar a detecção para cobrir o número de apps que são enviadas ao mercado. Neste artigo, o foco principal é o estudo da eficácia dos métodos automáticos de detecção de malware e as suas capacidades de acompanhar a popularidade de novo malware, bem como as suas tendências em constante mudança. Analisando o desempenho de algoritmos de ML treinados, com dados do mundo real, em diferentes períodos e escalas de tempo com recursos extraídos estaticamente, dinamicamente e com feedback do utilizador, é possível identificar a configuração ideal para maximizar a detecção de malware