234,009 research outputs found
Automatically Securing Permission-Based Software by Reducing the Attack Surface: An Application to Android
A common security architecture, called the permission-based security model
(used e.g. in Android and Blackberry), entails intrinsic risks. For instance,
applications can be granted more permissions than they actually need, what we
call a "permission gap". Malware can leverage the unused permissions for
achieving their malicious goals, for instance using code injection. In this
paper, we present an approach to detecting permission gaps using static
analysis. Our prototype implementation in the context of Android shows that the
static analysis must take into account a significant amount of
platform-specific knowledge. Using our tool on two datasets of Android
applications, we found out that a non negligible part of applications suffers
from permission gaps, i.e. does not use all the permissions they declare
Malware Detection Using Dynamic Analysis
In this research, we explore the field of dynamic analysis which has shown promis- ing results in the field of malware detection. Here, we extract dynamic software birth- marks during malware execution and apply machine learning based detection tech- niques to the resulting feature set. Specifically, we consider Hidden Markov Models and Profile Hidden Markov Models. To determine the effectiveness of this dynamic analysis approach, we compare our detection results to the results obtained by using static analysis. We show that in some cases, significantly stronger results can be obtained using our dynamic approach
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