1,156 research outputs found
A Survey of Techniques for Improving Security of GPUs
Graphics processing unit (GPU), although a powerful performance-booster, also
has many security vulnerabilities. Due to these, the GPU can act as a
safe-haven for stealthy malware and the weakest `link' in the security `chain'.
In this paper, we present a survey of techniques for analyzing and improving
GPU security. We classify the works on key attributes to highlight their
similarities and differences. More than informing users and researchers about
GPU security techniques, this survey aims to increase their awareness about GPU
security vulnerabilities and potential countermeasures
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
CSI Neural Network: Using Side-channels to Recover Your Artificial Neural Network Information
Machine learning has become mainstream across industries. Numerous examples
proved the validity of it for security applications. In this work, we
investigate how to reverse engineer a neural network by using only power
side-channel information. To this end, we consider a multilayer perceptron as
the machine learning architecture of choice and assume a non-invasive and
eavesdropping attacker capable of measuring only passive side-channel leakages
like power consumption, electromagnetic radiation, and reaction time.
We conduct all experiments on real data and common neural net architectures
in order to properly assess the applicability and extendability of those
attacks. Practical results are shown on an ARM CORTEX-M3 microcontroller. Our
experiments show that the side-channel attacker is capable of obtaining the
following information: the activation functions used in the architecture, the
number of layers and neurons in the layers, the number of output classes, and
weights in the neural network. Thus, the attacker can effectively reverse
engineer the network using side-channel information.
Next, we show that once the attacker has the knowledge about the neural
network architecture, he/she could also recover the inputs to the network with
only a single-shot measurement. Finally, we discuss several mitigations one
could use to thwart such attacks.Comment: 15 pages, 16 figure
The weak password problem: chaos, criticality, and encrypted p-CAPTCHAs
Vulnerabilities related to weak passwords are a pressing global economic and
security issue. We report a novel, simple, and effective approach to address
the weak password problem. Building upon chaotic dynamics, criticality at phase
transitions, CAPTCHA recognition, and computational round-off errors we design
an algorithm that strengthens security of passwords. The core idea of our
method is to split a long and secure password into two components. The first
component is memorized by the user. The second component is transformed into a
CAPTCHA image and then protected using evolution of a two-dimensional dynamical
system close to a phase transition, in such a way that standard brute-force
attacks become ineffective. We expect our approach to have wide applications
for authentication and encryption technologies.Comment: 5 pages, 6 figer
Quantifying Shannon's Work Function for Cryptanalytic Attacks
Attacks on cryptographic systems are limited by the available computational
resources. A theoretical understanding of these resource limitations is needed
to evaluate the security of cryptographic primitives and procedures. This study
uses an Attacker versus Environment game formalism based on computability logic
to quantify Shannon's work function and evaluate resource use in cryptanalysis.
A simple cost function is defined which allows to quantify a wide range of
theoretical and real computational resources. With this approach the use of
custom hardware, e.g., FPGA boards, in cryptanalysis can be analyzed. Applied
to real cryptanalytic problems, it raises, for instance, the expectation that
the computer time needed to break some simple 90 bit strong cryptographic
primitives might theoretically be less than two years.Comment: 19 page
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