![](bg9.png)
of the 9 proposed security rules are independent of
known malware request pattern, while 3 depends par-
tially on identified request patterns. Our analysis thus
provides a vision regarding the use of security rules
that are not completely dependent on known permis-
sion request patterns for the detection of malapps.
6 CONCLUSION
We have investigated the feasibility of characterizing
app behaviour using risk ratings from proposed im-
pact levels of Android permission. We demonstrate
that the risk signals can be used to assist layman users
in their security evaluation of Android apps. The first
rating is the sensitivity index of the app based on the
impact levels of requested permissions, the second ra-
ting is on its permission request compared to similar
apps, while the last rating is on its permission that
characterizes class label of apps as benign or malici-
ous. Our result demonstrates that the proposed fra-
mework can be used to improve risk signalling of An-
droid apps with a 95% accuracy.
For risk signalling, the ratings provide a measure
of awareness and scrutiny as a user defence against
malicious applications. For malware detection, a de-
tailed knowledge of app behaviour is essential for
signatures. Existing approaches and future analysis
could incorporate these ratings as the first phase in
prioritizing, especially when a large dataset of apps is
involved. For future work, we are exploring features
to make the risk ratings adjustable to contextual fac-
tors, such as actual usage of permissions in the source
code.
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