128 research outputs found
Nimbus: Toward Speed Up Function Signature Recovery via Input Resizing and Multi-Task Learning
Function signature recovery is important for many binary analysis tasks such
as control-flow integrity enforcement, clone detection, and bug finding.
Existing works try to substitute learning-based methods with rule-based methods
to reduce human effort.They made considerable efforts to enhance the system's
performance, which also bring the side effect of higher resource consumption.
However, recovering the function signature is more about providing information
for subsequent tasks, and both efficiency and performance are significant.
In this paper, we first propose a method called Nimbus for efficient function
signature recovery that furthest reduces the whole-process resource consumption
without performance loss. Thanks to information bias and task relation (i.e.,
the relation between parameter count and parameter type recovery), we utilize
selective inputs and introduce multi-task learning (MTL) structure for function
signature recovery to reduce computational resource consumption, and fully
leverage mutual information. Our experimental results show that, with only
about the one-eighth processing time of the state-of-the-art method, we even
achieve about 1% more prediction accuracy over all function signature recovery
tasks
PerfWeb: How to Violate Web Privacy with Hardware Performance Events
The browser history reveals highly sensitive information about users, such as
financial status, health conditions, or political views. Private browsing modes
and anonymity networks are consequently important tools to preserve the privacy
not only of regular users but in particular of whistleblowers and dissidents.
Yet, in this work we show how a malicious application can infer opened websites
from Google Chrome in Incognito mode and from Tor Browser by exploiting
hardware performance events (HPEs). In particular, we analyze the browsers'
microarchitectural footprint with the help of advanced Machine Learning
techniques: k-th Nearest Neighbors, Decision Trees, Support Vector Machines,
and in contrast to previous literature also Convolutional Neural Networks. We
profile 40 different websites, 30 of the top Alexa sites and 10 whistleblowing
portals, on two machines featuring an Intel and an ARM processor. By monitoring
retired instructions, cache accesses, and bus cycles for at most 5 seconds, we
manage to classify the selected websites with a success rate of up to 86.3%.
The results show that hardware performance events can clearly undermine the
privacy of web users. We therefore propose mitigation strategies that impede
our attacks and still allow legitimate use of HPEs
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