129 research outputs found
Interpretable Probabilistic Password Strength Meters via Deep Learning
Probabilistic password strength meters have been proved to be the most
accurate tools to measure password strength. Unfortunately, by construction,
they are limited to solely produce an opaque security estimation that fails to
fully support the user during the password composition. In the present work, we
move the first steps towards cracking the intelligibility barrier of this
compelling class of meters. We show that probabilistic password meters
inherently own the capability of describing the latent relation occurring
between password strength and password structure. In our approach, the security
contribution of each character composing a password is disentangled and used to
provide explicit fine-grained feedback for the user. Furthermore, unlike
existing heuristic constructions, our method is free from any human bias, and,
more importantly, its feedback has a clear probabilistic interpretation. In our
contribution: (1) we formulate the theoretical foundations of interpretable
probabilistic password strength meters; (2) we describe how they can be
implemented via an efficient and lightweight deep learning framework suitable
for client-side operability.Comment: An abridged version of this paper appears in the proceedings of the
25th European Symposium on Research in Computer Security (ESORICS) 202
Mesoscopic simulation study of wall roughness effects in micro-channel flows of dense emulsions
We study the Poiseuille flow of a soft-glassy material above the jamming
point, where the material flows like a complex fluid with Herschel- Bulkley
rheology. Microscopic plastic rearrangements and the emergence of their spatial
correlations induce cooperativity flow behavior whose effect is pronounced in
presence of confinement. With the help of lattice Boltzmann numerical
simulations of confined dense emulsions, we explore the role of geometrical
roughness in providing activation of plastic events close to the boundaries. We
probe also the spatial configuration of the fluidity field, a continuum
quantity which can be related to the rate of plastic events, thereby allowing
us to establish a link between the mesoscopic plastic dynamics of the jammed
material and the macroscopic flow behaviour
Fluidisation and plastic activity in a model soft-glassy material flowing in micro-channels with rough walls
By means of mesoscopic numerical simulations of a model soft-glassy material,
we investigate the role of boundary roughness on the flow behaviour of the
material, probing the bulk/wall and global/local rheologies. We show that the
roughness reduces the wall slip induced by wettability properties and acts as a
source of fluidisation for the material. A direct inspection of the plastic
events suggests that their rate of occurrence grows with the fluidity field,
reconciling our simulations with kinetic elasto-plastic descriptions of jammed
materials. Notwithstanding, we observe qualitative and quantitative differences
in the scaling, depending on the distance from the rough wall and on the
imposed shear. The impact of roughness on the orientational statistics is also
studied
Adversarial Out-domain Examples for Generative Models
Deep generative models are rapidly becoming a common tool for researchers and
developers. However, as exhaustively shown for the family of discriminative
models, the test-time inference of deep neural networks cannot be fully
controlled and erroneous behaviors can be induced by an attacker. In the
present work, we show how a malicious user can force a pre-trained generator to
reproduce arbitrary data instances by feeding it suitable adversarial inputs.
Moreover, we show that these adversarial latent vectors can be shaped so as to
be statistically indistinguishable from the set of genuine inputs. The proposed
attack technique is evaluated with respect to various GAN images generators
using different architectures, training processes and for both conditional and
not-conditional setups.Comment: accepted in proceedings of the Workshop on Machine Learning for
Cyber-Crime Investigation and Cybersecurit
Highly optimized simulations on single- and multi-GPU systems of 3D Ising spin glass
We present a highly optimized implementation of a Monte Carlo (MC) simulator
for the three-dimensional Ising spin-glass model with bimodal disorder, i.e.,
the 3D Edwards-Anderson model running on CUDA enabled GPUs. Multi-GPU systems
exchange data by means of the Message Passing Interface (MPI). The chosen MC
dynamics is the classic Metropolis one, which is purely dissipative, since the
aim was the study of the critical off-equilibrium relaxation of the system. We
focused on the following issues: i) the implementation of efficient access
patterns for nearest neighbours in a cubic stencil and for
lagged-Fibonacci-like pseudo-Random Numbers Generators (PRNGs); ii) a novel
implementation of the asynchronous multispin-coding Metropolis MC step allowing
to store one spin per bit and iii) a multi-GPU version based on a combination
of MPI and CUDA streams. We highlight how cubic stencils and PRNGs are two
subjects of very general interest because of their widespread use in many
simulation codes. Our code best performances ~3 and ~5 psFlip on a GTX Titan
with our implementations of the MINSTD and MT19937 respectively.Comment: 39 pages, 13 figure
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