Analysis of malicious and benign Android applications

Since its establishment, the Android applications market has been infected by a proliferation of malicious applications. Recent studies show that rogue developers are injecting malware into legitimate market applications which are then installed on open source sites for consumer uptake. Often, applications are infected several times. In this paper, we investigate the behavior of malicious Android applications, we present a simple and effective way to safely execute and analyze them. As part of this analysis, we use the Android application sandbox Droidbox to generate behavioral graphs for each sample and these provide the basis of the development of patterns to aid in identifying it. As a result, we are able to determine if family names have been correctly assigned by current anti-virus vendors. Our results indicate that the traditional anti-virus mechanisms are not able to correctly identify malicious Android applications.

Zero permission android applications - attacks and defenses

Google advertises the Android permission framework as one of the core security features present on its innovative and flexible mobile platform. The permissions are a means to control access to restricted AP/s and system resources. However, there are Android applications which do not request permissions at all.In this paper, we analyze the repercussions of installing an Android application that does not include any permission and the types of sensitive information that can be accessed by such an application. We found that even app/icaaons with no permissions are able to access sensitive information (such the device ID) and transmit it to third-parties.

Contrasting permission patterns between clean and malicious android applications

The Android platform uses a permission system model to allow users and developers to regulate access to private information and system resources required by applications. Permissions have been proved to be useful for inferring behaviors and characteristics of an application. In this paper, a novel method to extract contrasting permission patterns for clean and malicious applications is proposed. Contrary to existing work, both required and used permissions were considered when discovering the patterns. We evaluated our methodology on a clean and a malware dataset, each comprising of 1227 applications. Our empirical results suggest that our permission patterns can capture key differences between clean and malicious applications, which can assist in characterizing these two types of applications.

Android applications : Data leaks via advertising libraries

Recent studies have determined that many Android applications in both official and non-official online markets expose details of the users' smartphones without user consent. In this paper, we explain why such applications leak, how they leak and where the data is leaked to. In order to achieve this, we combine static and dynamic analysis to examine Java classes and application behaviour for a set of popular, clean applications from the Finance and Games categories. We observed that all the applications in our data set which leaked information (10%) had third-party advertising libraries embedded in their respective Java packages.

Mining permission patterns for contrasting clean and malicious android applications

An Android application uses a permission system to regulate the access to system resources and users' privacy-relevant information. Existing works have demonstrated several techniques to study the required permissions declared by the developers, but little attention has been paid towards used permissions. Besides, no specific permission combination is identified to be effective for malware detection. To fill these gaps, we have proposed a novel pattern mining algorithm to identify a set of contrast permission patterns that aim to detect the difference between clean and malicious applications. A benchmark malware dataset and a dataset of 1227 clean applications has been collected by us to evaluate the performance of the proposed algorithm. Valuable findings are obtained by analyzing the returned contrast permission patterns. © 2013 Elsevier B.V. All rights reserved.

Android malware detection with contrasting permission patterns

As the risk of malware is sharply increasing in Android platform, Android malware detection has become an important research topic. Existing works have demonstrated that required permissions of Android applications are valuable for malware analysis, but how to exploit those permission patterns for malware detection remains an open issue. In this paper, we introduce the contrasting permission patterns to characterize the essential differences between malwares and clean applications from the permission aspect. Then a framework based on contrasting permission patterns is presented for Android malware detection. According to the proposed framework, an ensemble classifier, Enclamald, is further developed to detect whether an application is potentially malicious. Every contrasting permission pattern is acting as a weak classifier in Enclamald, and the weighted predictions of involved weak classifiers are aggregated to the final result. Experiments on real-world applications validate that the proposed Enclamald classifier outperforms commonly used classifiers for Android Malware Detection.

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.

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.

Applications of a TransFLash in an embedded system debugging

In this paper, the hardware and software design for using a TF card in debugging an embedded system are described. The used hardware platform is designed based on a PXA310 application processor. The Android open source operating system is used as the software platform. The design of the connection circuit between the application processor and the TF card is introduced first. Secondly, the design of the TF card driver program and the method for Android system to mount the TF card are described. In designing the TF driver program, an SPI operation mode and FAT32 file system are used. The transplant of the FAT32 file system is presented more detail. Finally, the paper introduced the system debugging and the test results are given for the TF card used in a video data acquisition unit of a video monitoring. It is shown that high speed data exchange and good universal property can be obtained by using a TF card to download a system image during developing and debugging. The TF card used in debugging can be used as a mass storage in the embedded product without the need of changing the design for debugging the system and it is also convenient for a user to upgrade operating system.

Security testing in Android networks : a practical case study

Android is a new generation of an open operating system directed at mobile devices that are carried every day. The openness of this architecture is leading to new applications and opportunities including a host of multimedia services, new interfaces and browsers, multitasking including support for wireless local, personal and wide area networking services. Security with mobility and wireless connectivity thus becomes even more important with all these exciting developments. Vital security issues such as leakage of private information, file stealing and spambots abound in networks in practice and Android networks continue to be subject to these same families of vulnerabilities. This paper provides a demonstration of such vulnerabilities in spite of the best efforts of designers and implementers. In particular it describes examples of data leakage and file stealing (address books, contact lists, SMS messages, pictures) as well as demonstrating how Android devices can create spambots. © 2013 IEEE.

Smartphone applications, malware and data theft

The growing popularity of smartphone devices has led to development of increasing numbers of applications which have subsequently become targets for malicious authors. Analysing applications in order to identify malicious ones is a current major concern in information security; an additional problem connected with smart-phone applications is that their many advertising libraries can lead to loss of personal information. In this paper, we relate the current methods of detecting malware on smartphone devices and discuss the problems caused by malware as well as advertising.