10 research outputs found
Optimal Active Social Network De-anonymization Using Information Thresholds
In this paper, de-anonymizing internet users by actively querying their group
memberships in social networks is considered. In this problem, an anonymous
victim visits the attacker's website, and the attacker uses the victim's
browser history to query her social media activity for the purpose of
de-anonymization using the minimum number of queries. A stochastic model of the
problem is considered where the attacker has partial prior knowledge of the
group membership graph and receives noisy responses to its real-time queries.
The victim's identity is assumed to be chosen randomly based on a given
distribution which models the users' risk of visiting the malicious website. A
de-anonymization algorithm is proposed which operates based on information
thresholds and its performance both in the finite and asymptotically large
social network regimes is analyzed. Furthermore, a converse result is provided
which proves the optimality of the proposed attack strategy
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
Undermining User Privacy on Mobile Devices Using AI
Over the past years, literature has shown that attacks exploiting the
microarchitecture of modern processors pose a serious threat to the privacy of
mobile phone users. This is because applications leave distinct footprints in
the processor, which can be used by malware to infer user activities. In this
work, we show that these inference attacks are considerably more practical when
combined with advanced AI techniques. In particular, we focus on profiling the
activity in the last-level cache (LLC) of ARM processors. We employ a simple
Prime+Probe based monitoring technique to obtain cache traces, which we
classify with Deep Learning methods including Convolutional Neural Networks. We
demonstrate our approach on an off-the-shelf Android phone by launching a
successful attack from an unprivileged, zeropermission App in well under a
minute. The App thereby detects running applications with an accuracy of 98%
and reveals opened websites and streaming videos by monitoring the LLC for at
most 6 seconds. This is possible, since Deep Learning compensates measurement
disturbances stemming from the inherently noisy LLC monitoring and unfavorable
cache characteristics such as random line replacement policies. In summary, our
results show that thanks to advanced AI techniques, inference attacks are
becoming alarmingly easy to implement and execute in practice. This once more
calls for countermeasures that confine microarchitectural leakage and protect
mobile phone applications, especially those valuing the privacy of their users
Side Channels in the Cloud: Isolation Challenges, Attacks, and Countermeasures
Cloud computing is based on the sharing of physical resources among several virtual machines through a virtualization layer providing software isolation. Despite advances in virtualization, data security and isolation guarantees remain important challenges for cloud providers. Some of the most prominent isolation violations come from side-channel attacks that aim at exploiting and using a leaky channel to obtain sensitive data such as encryption keys. Such channels may be created by vulnerable implementations of cryptographic algorithms, exploiting weaknesses of processor architectures or of resource sharing in the virtualization layer. In this paper, we provide a comprehensive survey of side-channel attacks (SCA) and mitigation techniques for virtualized environments, focusing on cache-based attacks. We review isolation challenges, attack classes and techniques. We also provide a layer-based taxonomy of applicable countermeasures , from the hardware to the application level, with an assessment of their effectiveness