Towards Privacy Risk Analysis in Android Applications Using Machine Learning Approaches

Android-based devices easily fall prey to an attack due to its free availability in the android market. These Android applications are not certified by the legitimate organization. If the user cannot distinguish between the set of permissions requested by an application and its risk, then an attacker can easily exploit the permissions to propagate malware. In this article, the authors present an approach for privacy risk analysis in Android applications using machine learning. The proposed approach can analyse and identify the malware application permissions. Here, the authors achieved high accuracy and improved F-measure through analyzing the proposed method on the M0Droid dataset and completed testing on an extensive test set with malware from the Androzoo dataset and benign applications from the Drebin dataset.

Android Application Security

Android is a free, open source platform that allows any developer to submit apps to the Android Market with no restrictions. This enables hackers to pass their malicious apps to the Android Market as legitimate apps. The central issue lies at the heart of the Android permission mechanism, which is not capable of blocking malicious apps from accessing sensitive phone resources (e.g., contact info and browsing history); it either allows or disallows apps from accessing the resources requested by the app at the installation time. This chapter investigated the scope of this issue and concluded that hackers use malicious apps as attack vectors to compromise Android smartphones and steal confidential data and that no security solutions exist to combat malicious apps. The researcher suggested designing a real time monitoring application to detect and deter malicious apps from compromising users' sensitive data; such application is necessary for Android users to protect their privacy and prevent financial loss.

Smartphone Confrontational Applications and Security Issues

In today's society, there is a high volume of smartphones, with Android being the most popular and most commonly used smartphones. In the last few years, the Android market has been booming, making lots of developers join the industry so as to create various mobile applications that are a benefit to people's lives. However, its over-popularity has brought many crime issues, including security. One of the major common incidents to mobile users is having their mobile phones lost or stolen. Since most mobile users want to find their lost phones, they are looking for the most reliable features that can help them locate their smartphones. Luckily, there are some developed applications and services that have been designed to track down and locate lost or stolen smartphones. In this work, the authors tried to identify a collection of these applications and the information they send to the user in aiding them to find their phone. Since some applications are able to send location information or a photo, this work will look at what metadata is usually sent with the message.

Smartphone Confrontational Applications and Security Issues

In today's society, there is a high volume of smartphones, with Android being the most popular and most commonly used smartphones. In the last few years, the Android market has been booming, making lots of developers join the industry so as to create various mobile applications that are a benefit to people's lives. However, its over-popularity has brought many crime issues, including security. One of the major common incidents to mobile users is having their mobile phones lost or stolen. Since most mobile users want to find their lost phones, they are looking for the most reliable features that can help them locate their smartphones. Luckily, there are some developed applications and services that have been designed to track down and locate lost or stolen smartphones. In this work, the authors tried to identify a collection of these applications and the information they send to the user in aiding them to find their phone. Since some applications are able to send location information or a photo, this work will look at what metadata is usually sent with the message.

Malicious Application Detection and Classification System for Android Mobiles

The Android Mobiles constitute a large portion of mobile market which also attracts the malware developer for malicious gains. Every year hundreds of malwares are detected in the Android market. Unofficial and Official Android market such as Google Play Store are infested with fake and malicious apps which is a warning alarm for naive user. Guided by this insight, this paper presents the malicious application detection and classification system using machine learning techniques by extracting and analyzing the Android Permission Feature of the Android applications. For the feature extraction, the authors of this work have developed the AndroData tool written in shell script and analyzed the extracted features of 1060 Android applications with machine learning algorithms. They have achieved the malicious application detection and classification accuracy of 98.2% and 87.3%, respectively with machine learning techniques.

When Private Information Settles the Bill: Money and Privacy in Google’s Market for Smartphone Applications

We shed light on a money-for-privacy trade-off in the market for smartphone applications (“apps”). Developers offer their apps at lower prices in return for greater access to personal information, and consumers choose between low prices and more privacy. We provide evidence for this pattern using data from 300,000 apps obtained from the Google Play Store (formerly Android Market) in 2012 and 2014. Our findings show that the market’s supply and demand sides both consider an app’s ability to collect private information, measured by the apps’s use of privacy-sensitive permissions: (1) cheaper apps use more privacy-sensitive permissions; (2) given price and functionality, demand is lower for apps with sensitive permissions; and (3) the strength of this relationship depends on contextual factors, such as the targeted user group, the app’s previous success, and its category. Our results are robust and consistent across several robustness checks, including the use of panel data, a difference-in-differences analysis, “twin” pairs of apps, and various measures of privacy-sensitivity and app demand. This paper was accepted by Anandhi Bharadwaj, information systems.

Towards Privacy Risk Analysis in Android Applications Using Machine Learning Approaches

Android-based devices easily fall prey to an attack due to its free availability in the android market. These Android applications are not certified by the legitimate organization. If the user cannot distinguish between the set of permissions requested by an application and its risk, then an attacker can easily exploit the permissions to propagate malware. In this article, the authors present an approach for privacy risk analysis in Android applications using machine learning. The proposed approach can analyse and identify the malware application permissions. Here, the authors achieved high accuracy and improved F-measure through analyzing the proposed method on the M0Droid dataset and completed testing on an extensive test set with malware from the Androzoo dataset and benign applications from the Drebin dataset.

Android Application Security

Android is a free, open source platform that allows any developer to submit apps to the Android Market with no restrictions. This enables hackers to pass their malicious apps to the Android Market as legitimate apps. The central issue lies at the heart of the Android permission mechanism, which is not capable of blocking malicious apps from accessing sensitive phone resources (e.g., contact info and browsing history); it either allows or disallows apps from accessing the resources requested by the app at the installation time. This chapter investigated the scope of this issue and concluded that hackers use malicious apps as attack vectors to compromise Android smartphones and steal confidential data and that no security solutions exist to combat malicious apps. The researcher suggested designing a real time monitoring application to detect and deter malicious apps from compromising users' sensitive data; such application is necessary for Android users to protect their privacy and prevent financial loss.