2,071 research outputs found
Adversarial Robustness Assessment of NeuroEvolution Approaches
NeuroEvolution automates the generation of Artificial Neural Networks through
the application of techniques from Evolutionary Computation. The main goal of
these approaches is to build models that maximize predictive performance,
sometimes with an additional objective of minimizing computational complexity.
Although the evolved models achieve competitive results performance-wise, their
robustness to adversarial examples, which becomes a concern in
security-critical scenarios, has received limited attention. In this paper, we
evaluate the adversarial robustness of models found by two prominent
NeuroEvolution approaches on the CIFAR-10 image classification task: DENSER and
NSGA-Net. Since the models are publicly available, we consider white-box
untargeted attacks, where the perturbations are bounded by either the L2 or the
Linfinity-norm. Similarly to manually-designed networks, our results show that
when the evolved models are attacked with iterative methods, their accuracy
usually drops to, or close to, zero under both distance metrics. The DENSER
model is an exception to this trend, showing some resistance under the L2
threat model, where its accuracy only drops from 93.70% to 18.10% even with
iterative attacks. Additionally, we analyzed the impact of pre-processing
applied to the data before the first layer of the network. Our observations
suggest that some of these techniques can exacerbate the perturbations added to
the original inputs, potentially harming robustness. Thus, this choice should
not be neglected when automatically designing networks for applications where
adversarial attacks are prone to occur
Spontaneous formation of domain wall lattices in two spatial dimensions
We show that the process of spontaneous symmetry breaking can trap a field
theoretic system in a highly non-trivial state containing a lattice of domain
walls. In one large compact space dimension, a lattice is inevitably formed. In
two dimensions, the probability of lattice formation depends on the ratio of
sizes L_x, L_y of the spatial dimensions. We find that a lattice can form even
if R=L_y/L_x is of order unity. We numerically determine the number of walls in
the lattice as a function of L_x and L_y.Comment: 6 pages, 6 figures. Background material added and minor corrections
included. Final version to be published in Phys. Rev.
The Thermodynamics of Cosmic String densities in U(1) Scalar Field Theory
We present a full characterization of the phase transition in U(1) scalar
field theory and of the associated vortex string thermodynamics in 3D. We show
that phase transitions in the string densities exist and measure their critical
exponents, both for the long string and the short loops. Evidence for a natural
separation between these two string populations is presented. In particular our
results strongly indicate that an infinite string population will only exist
above the critical temperature. Canonical initial conditions for cosmic string
evolution are show to correspond to the infinite temperature limit of the
theory.Comment: 4 pages, 4 figures, RevTe
The Portuguese contemporary art as an investment
In this paper we investigate what drives the
prices of Portuguese contemporary art at auction and explore the potential of art as
an asset.
Based on a hedonic prices model we construct
an Art Price Index as a proxy for the Portuguese contemporary art market over the period of 1994 to 2014.
A performance analysis suggests that art underperforms the S&P500 but overperforms the
Portuguese stock market and American Government bonds.
However, It does it at the cost of higher risk.
Results also show that art as low correlation
with financial markets, evidencing some potential in risk mitigation when added to
traditional equity portfolios.NSBE - UN
MS IPTV audit collection services
Tese de mestrado em Segurança Informática, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2011Microsoft Mediaroom Internet Protocol Television (MS IPTV), uma plataforma de televisão digital, levou o conceito de televisão a uma dimensão totalmente nova. MS IPTV é um sistema onde o serviço de televisão digital é entregue aos clientes usando Internet Protocol (IP), através de uma conexão de banda larga. Com o advento do IPTV começaram a aparecer novas situações relacionadas com a segurança da televisão, uma vez que, a infra-estrutura começou a ganhar complexidade e exposição a uma série de novos riscos. Por esta razão, a segurança numa infra-estrutura de MS IPTV não é apenas mais uma funcionalidade, mas sim uma necessidade. Podemos mesmo dizer que hoje em dia é obrigatório aguçar o engenho para estar um passo à frente dos atacantes, uma vez que estes estão sempre à espera de uma brecha, para comprometer os sistemas. Uma infra-estrutura como o MS IPTV armazena por omissão dados relativos ao comportamento dos utilizadores ao nível dos logs, no entanto esta informação só se torna relevante se puder ser consultada e analisada com o objetivo de proporcionar uma compreensão a alto nível sobre os diferentes padrões que estão a ocorrer nos servidores ou no comportamento dos utilizadores, uma tarefa que envolve poderosas técnicas de data parsing.
A tese apresenta uma abordagem que combina técnicas de data parsing, a fim de analisar os logs relevantes da infra-estrutura de MS IPTV, com o objetivo principal de aumentar a segurança através da investigação dos tipos de informações adicionais que pode ser extraída.
Tentámos assim entender se é possível determinar que tipos de ataques estão a ser perpetrados contra a infra-estrutura MS IPTV, com base na análise dos logs. Como o foco central desta tese está no diagnóstico, propomos uma abordagem para descobrir ataques, onde os logs são verificados para identificar grupos coerentes de ocorrências susceptíveis de constituir ataques que apelidámos de padrões. Nos testes, verificámos que a nossa abordagem consegue bons resultados na descoberta de ataques. Os resultados obtidos têm a vantagem adicional de poderem ser integrados na ferramenta de monitorização utilizada pelas equipas de operação dos sistemas da Portugal Telecom, o System Center Operations Manager (SCOM).Microsoft Mediaroom Internet Protocol TeleVision (MS IPTV), one of the platforms for digital TV, took television to an all new dimension level. MS IPTV is described as a system where a digital television service is delivered to consumers using the Internet Protocol over a broadband connection. Since the infrastructure started to gain complexity and exposure to a number of new risks, never envisaged situations related to television security started to appear. For this reason, MS IPTV security is not only a great asset, but also a necessity. Nowadays it is mandatory to sharpen the wit to get ahead of attackers, who are always waiting for a breach to compromise our systems.
MS IPTV log servers collect information about user and system behavior. However, this information only becomes relevant if it can be queried and analyzed with the purpose of providing high-level understanding about the different patterns. This task must comprise powerful data parsing techniques, since MS IPTV is able to generate close to one terabyte of logs per day.
This thesis presents an approach that combines data parsing techniques in order to analyze relevant MS IPTV logs, with the main objective to increase security through the investigation of what type of additional information can be extracted from the server log files of a MS IPTV platform. The thesis focus is on diagnosis, trying to understand if it is possible to determine what type of attacks are being perpetrated against the MS IPTV infrastructure.
We propose an approach for discovering attacks, where the application logs are scanned to identify coherent groups of occurrences that we call patterns, which are likely to constitute attacks. Our results showed that our approach achieves good results in discovering potential attacks. Our output results can be integrated into the MS IPTV monitoring system tool SCOM (System Center Operations Manager), which is an additional advantage over the other monitoring and log management systems
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