Towards Automated Android App Collusion Detection

Android OS supports multiple communication methods between apps. This opens the possibility to carry out threats in a collaborative fashion, c.f. the Soundcomber example from 2011. In this paper we provide a concise definition of collusion and report on a number of automated detection approaches, developed in co-operation with Intel Security

Detection of app collusion potential using logic programming

Mobile devices pose a particular security risk because they hold personal details (accounts, locations, contacts, photos) and have capabilities potentially exploitable for eavesdropping (cameras/microphone, wireless connections). The Android operating system is designed with a number of built-in security features such as application sandboxing and permission-based access control. Unfortunately, these restrictions can be bypassed, without the user noticing, by colluding apps whose combined permissions allow them to carry out attacks that neither app is able to execute by itself. While the possibility of app collusion was first warned in 2011, it has been unclear if collusion is used by malware in the wild due to a lack of suitable detection methods and tools. This paper describes how we found the first collusion in the wild. We also present a strategy for detecting collusions and its implementation in Prolog that allowed us to make this discovery. Our detection strategy is grounded in concise definitions of collusion and the concept of ASR (Access-Send-Receive) signatures. The methodology is supported by statistical evidence. Our approach scales and is applicable to inclusion into professional malware detection systems: we applied it to a set of more than 50,000 apps collected in the wild. Code samples of our tool as well as of the detected malware are available

Towards a threat assessment framework for apps collusion

App collusion refers to two or more apps working together to achieve a malicious goal that they otherwise would not be able to achieve individually. The permissions based security model of Android does not address this threat as it is rather limited to mitigating risks of individual apps. This paper presents a technique for quantifying the collusion threat, essentially the first step towards assessing the collusion risk. The proposed method is useful in finding the collusion candidate of interest which is critical given the high volume of Android apps available. We present our empirical analysis using a classified corpus of over 29,000 Android apps provided by Intel SecurityTM

Towards a threat assessment framework for apps collusion

App collusion refers to two or more apps working together to achieve a malicious goal that they otherwise would not be able to achieve individually. The permissions based security model of Android does not address this threat as it is rather limited to mitigating risks of individual apps. This paper presents a technique for quantifying the collusion threat, essentially the first step towards assessing the collusion risk. The proposed method is useful in finding the collusion candidate of interest which is critical given the high volume of Android apps available. We present our empirical analysis using a classified corpus of over 29,000 Android apps provided by Intel SecurityTM

Automated generation of colluding apps for experimental research

Colluding apps bypass the security measures enforced by sandboxed operating systems such as Android. App collusion can be a real threat in cloud environments as well. Research in detecting and protecting against app collusion requires a variety of colluding apps for experimentation. Presently the number of (real or manually crafted) apps available to researchers is very limited. In this paper we propose a system called Application Collusion Engine (ACE) to automatically generate combinations of colluding and non-colluding Android apps to help researchers fairly evaluate different collusion detection and protection methods. Our initial implementation includes a variety of components that enable the system to create more than 5,000 different colluding and non-colluding app sets. ACE can be extended with more functional components to create even more colluding apps. To show the usefulness of our system, we have applied different risk evaluation and collusion detection methods to the created set of colluding apps

Compartmentation policies for Android apps:A combinatorial optimization approach

Some smartphone platforms such as Android have a distinctive message passing system that allows for sophisticated interactions among app components, both within and across app boundaries. This gives rise to various security and privacy risks, including not only intentional collusion attacks via permission re-delegation but also inadvertent disclosure of information and service misuse through confused deputy attacks. In this paper, we revisit the perils of app coexistence in the same platform and propose a risk mitigation mechanism based on segregating apps into isolated groups following classical security compartmentation principles. Compartments can be implemented using lightweight approaches such as Inter-Component Communication (ICC) firewalling or through virtualization, effectively fencing off each group of apps. We then leverage recent works on quantified risk metrics for Android apps to couch compartmentation as a combinatorial optimization problem akin to the classical bin packing or knapsack problems. We study a number of simple yet effective numerical optimization heuristics, showing that very good compartmentation solutions can be obtained for the problem sizes expected in current’s mobile environments