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Environmental Authentication in Malware
Malware needs to execute on a target machine while simultaneously keeping its payload confidential from a malware analyst. Standard encryption can be used to ensure the confidentiality, but it does not address the problem of hiding the key. Any analyst can find the decryption key if it is stored in the malware or derived in plain view.
One approach is to derive the key from a part of the environment which changes when the analyst is present. Such malware derives a key from the environment and encrypts its true functionality under this key.
In this paper, we present a formal framework for environmental authentication. We formalize the interaction between malware and analyst in three settings: 1) blind: in which the analyst does not have access to the target environment, 2) basic: where the analyst can load a single analysis toolkit on an effected target, and 3) resettable: where the analyst can create multiple copies of an infected environment. We show necessary and sufficient conditions for malware security in the blind and basic games and show that even under mild conditions, the analyst can always win in the resettable scenario
Using Hover to Compromise the Confidentiality of User Input on Android
We show that the new hover (floating touch) technology, available in a number
of today's smartphone models, can be abused by any Android application running
with a common SYSTEM_ALERT_WINDOW permission to record all touchscreen input
into other applications. Leveraging this attack, a malicious application
running on the system is therefore able to profile user's behavior, capture
sensitive input such as passwords and PINs as well as record all user's social
interactions. To evaluate our attack we implemented Hoover, a proof-of-concept
malicious application that runs in the system background and records all input
to foreground applications. We evaluated Hoover with 40 users, across two
different Android devices and two input methods, stylus and finger. In the case
of touchscreen input by finger, Hoover estimated the positions of users' clicks
within an error of 100 pixels and keyboard input with an accuracy of 79%.
Hoover captured users' input by stylus even more accurately, estimating users'
clicks within 2 pixels and keyboard input with an accuracy of 98%. We discuss
ways of mitigating this attack and show that this cannot be done by simply
restricting access to permissions or imposing additional cognitive load on the
users since this would significantly constrain the intended use of the hover
technology.Comment: 11 page
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