Permission-based Mechanism Research Articles

Malware Detection Using Machine Learning and Deep Learning

Abstract: Android application security is based on permission-based mechanisms that restrict third-party Android applications’ access to critical resources on an Android device. The user must accept a set of permissions required by the application before proceeding with the installation. This process is intended to inform users about the risks of installing and using applications on their devices. However, most of the time, even with a well-understood permission system, users are not fully aware of endangered threats, relying on application stores or the popularity of applications and relying on developers to You are accepting the install without trying to analyze your intent.

Malware Detection Using Machine Learning and Deep Learning

Abstract: Android application security is based on permission-based mechanisms that restrict third-party Android applications' access to critical resources on an Android device. The user must accept a set of permissions required by the application before proceeding with the installation. This process is intended to inform users about the risks of installing and using applications on their devices. However, most of the time, even with a well-understood permission system, users are not fully aware of endangered threats, relying on application stores or the popularity of applications and relying on developers to You are accepting the install without trying to analyze your intent.

Construction and Mitigation of User-Behavior-Based Covert Channels on Smartphones

To protect user privacy, many smartphone systems adopt the permission-based mechanism in which a user can evaluate the risk of requests for private information from a mobile app before installing it. However, recent studies show that the permission based mechanism is vulnerable to application collusion attacks because two apps, which appear to be harmless individually, can establish a covert channel and use it to leak confidential information. Consequently, people have designed some covert channel detection schemes, by checking abnormal status of the phone. In this paper, we point out that existing covert channel detection schemes may fail to detect a new type of collusion attacks referred as user-behavior-based covert channels. We implement three covert channels on Android smartphones. Our work sets a new alarm for the security issue of using smartphones. We then study the countermeasures to this new type of covert channels. Instead of trying to directly detect the proposed new type of covert channels, we propose two mitigation solutions to reduce the effectiveness of such covert channels. The mitigation solutions are also valid to other existing sensor-based side channels and/or covert channels on the phone.

Application Collusion Attack on the Permission-Based Security Model and its Implications for Modern Smartphone Systems

We show that the way in which permission-based mechanisms are used on today's mobile platforms enables attacks by colluding applications that communicate over overt and covert communication channels. These attacks allow applications to indirectly execute operations that those applications, based on their declared permissions, should not be able to execute. Example operations include disclosure of users private data (e.g., phone book and calendar entries) to remote parties by applications that do not have direct access to such data or cannot directly establish remote connections. We further show that on today’s mobile platforms users are not made aware of possible implications of application collusion--quite the contrary--users are implicitly lead to believe that by approving the installation of each application independently, based on its declared permissions, they can limit the damage that an application can cause. In this work, we show that this is not correct and that application permissions should be displayed to the users differently (e.g., in their aggregated form), reflecting their actual implications. We demonstrate the practicality of application collusion attacks by implementing several applications and example covert channels on an Android platform and an example channel on a Windows Phone 7 platform. We study free applications from the Android market and show that the potential for application collusion is significant. Finally, we discuss countermeasures that can be used to mitigate these attacks.