91,431 research outputs found
Role based behavior analysis
Tese de mestrado, Segurança Informática, Universidade de Lisboa, Faculdade de CiĂŞncias, 2009Nos nossos dias, o sucesso de uma empresa depende da sua agilidade e capacidade de se adaptar a condições que se alteram rapidamente. Dois requisitos para esse sucesso sĂŁo trabalhadores proactivos e uma infra-estrutura ágil de Tecnologias de InformacĂŁo/Sistemas de Informação (TI/SI) que os consiga suportar. No entanto, isto nem sempre sucede. Os requisitos dos utilizadores ao nĂvel da rede podem nao ser completamente conhecidos, o que causa atrasos nas mudanças de local e reorganizações. AlĂ©m disso, se nĂŁo houver um conhecimento preciso dos requisitos, a infraestrutura de TI/SI poderá ser utilizada de forma ineficiente, com excessos em algumas áreas e deficiĂŞncias noutras. Finalmente, incentivar a proactividade nĂŁo implica acesso completo e sem restrições, uma vez que pode deixar os sistemas vulneráveis a ameaças externas e internas. O objectivo do trabalho descrito nesta tese Ă© desenvolver um sistema que consiga caracterizar o comportamento dos utilizadores do ponto de vista da rede. Propomos uma arquitectura de sistema modular para extrair informação de fluxos de rede etiquetados. O processo Ă© iniciado com a criação de perfis de utilizador a partir da sua informação de fluxos de rede. Depois, perfis com caracterĂsticas semelhantes sĂŁo agrupados automaticamente, originando perfis de grupo. Finalmente, os perfis individuais sĂŁo comprados com os perfis de grupo, e os que diferem significativamente sĂŁo marcados como anomalias para análise detalhada posterior. Considerando esta arquitectura, propomos um modelo para descrever o comportamento de rede dos utilizadores e dos grupos. Propomos ainda mĂ©todos de visualização que permitem inspeccionar rapidamente toda a informação contida no modelo. O sistema e modelo foram avaliados utilizando um conjunto de dados reais obtidos de um operador de telecomunicações. Os resultados confirmam que os grupos projectam com precisĂŁo comportamento semelhante. AlĂ©m disso, as anomalias foram as esperadas, considerando a população subjacente. Com a informação que este sistema consegue extrair dos dados em bruto, as necessidades de rede dos utilizadores podem sem supridas mais eficazmente, os utilizadores suspeitos sĂŁo assinalados para posterior análise, conferindo uma vantagem competitiva a qualquer empresa que use este sistema.In our days, the success of a corporation hinges on its agility and ability to adapt to fast changing conditions. Proactive workers and an agile IT/IS infrastructure that can support them is a requirement for this success. Unfortunately, this is not always the case. The user’s network requirements may not be fully understood, which slows down relocation and reorganization. Also, if there is no grasp on the real requirements, the IT/IS infrastructure may not be efficiently used, with waste in some areas and deficiencies in others. Finally, enabling proactivity does not mean full unrestricted access, since this may leave the systems vulnerable to outsider and insider threats. The purpose of the work described on this thesis is to develop a system that can characterize user network behavior. We propose a modular system architecture to extract information from tagged network flows. The system process begins by creating user profiles from their network flows’ information. Then, similar profiles are automatically grouped into clusters, creating role profiles. Finally, the individual profiles are compared against the roles, and the ones that differ significantly are flagged as anomalies for further inspection. Considering this architecture, we propose a model to describe user and role network behavior. We also propose visualization methods to quickly inspect all the information contained in the model. The system and model were evaluated using a real dataset from a large telecommunications operator. The results confirm that the roles accurately map similar behavior. The anomaly results were also expected, considering the underlying population. With the knowledge that the system can extract from the raw data, the users network needs can be better fulfilled, the anomalous users flagged for inspection, giving an edge in agility for any company that uses it
The Error is the Feature: how to Forecast Lightning using a Model Prediction Error
Despite the progress within the last decades, weather forecasting is still a
challenging and computationally expensive task. Current satellite-based
approaches to predict thunderstorms are usually based on the analysis of the
observed brightness temperatures in different spectral channels and emit a
warning if a critical threshold is reached. Recent progress in data science
however demonstrates that machine learning can be successfully applied to many
research fields in science, especially in areas dealing with large datasets. We
therefore present a new approach to the problem of predicting thunderstorms
based on machine learning. The core idea of our work is to use the error of
two-dimensional optical flow algorithms applied to images of meteorological
satellites as a feature for machine learning models. We interpret that optical
flow error as an indication of convection potentially leading to thunderstorms
and lightning. To factor in spatial proximity we use various manual convolution
steps. We also consider effects such as the time of day or the geographic
location. We train different tree classifier models as well as a neural network
to predict lightning within the next few hours (called nowcasting in
meteorology) based on these features. In our evaluation section we compare the
predictive power of the different models and the impact of different features
on the classification result. Our results show a high accuracy of 96% for
predictions over the next 15 minutes which slightly decreases with increasing
forecast period but still remains above 83% for forecasts of up to five hours.
The high false positive rate of nearly 6% however needs further investigation
to allow for an operational use of our approach.Comment: 10 pages, 7 figure
How Advanced Change Patterns Impact the Process of Process Modeling
Process model quality has been an area of considerable research efforts. In
this context, correctness-by-construction as enabled by change patterns
provides promising perspectives. While the process of process modeling (PPM)
based on change primitives has been thoroughly investigated, only little is
known about the PPM based on change patterns. In particular, it is unclear what
set of change patterns should be provided and how the available change pattern
set impacts the PPM. To obtain a better understanding of the latter as well as
the (subjective) perceptions of process modelers, the arising challenges, and
the pros and cons of different change pattern sets we conduct a controlled
experiment. Our results indicate that process modelers face similar challenges
irrespective of the used change pattern set (core pattern set versus extended
pattern set, which adds two advanced change patterns to the core patterns set).
An extended change pattern set, however, is perceived as more difficult to use,
yielding a higher mental effort. Moreover, our results indicate that more
advanced patterns were only used to a limited extent and frequently applied
incorrectly, thus, lowering the potential benefits of an extended pattern set
Adversarially Tuned Scene Generation
Generalization performance of trained computer vision systems that use
computer graphics (CG) generated data is not yet effective due to the concept
of 'domain-shift' between virtual and real data. Although simulated data
augmented with a few real world samples has been shown to mitigate domain shift
and improve transferability of trained models, guiding or bootstrapping the
virtual data generation with the distributions learnt from target real world
domain is desired, especially in the fields where annotating even few real
images is laborious (such as semantic labeling, and intrinsic images etc.). In
order to address this problem in an unsupervised manner, our work combines
recent advances in CG (which aims to generate stochastic scene layouts coupled
with large collections of 3D object models) and generative adversarial training
(which aims train generative models by measuring discrepancy between generated
and real data in terms of their separability in the space of a deep
discriminatively-trained classifier). Our method uses iterative estimation of
the posterior density of prior distributions for a generative graphical model.
This is done within a rejection sampling framework. Initially, we assume
uniform distributions as priors on the parameters of a scene described by a
generative graphical model. As iterations proceed the prior distributions get
updated to distributions that are closer to the (unknown) distributions of
target data. We demonstrate the utility of adversarially tuned scene generation
on two real-world benchmark datasets (CityScapes and CamVid) for traffic scene
semantic labeling with a deep convolutional net (DeepLab). We realized
performance improvements by 2.28 and 3.14 points (using the IoU metric) between
the DeepLab models trained on simulated sets prepared from the scene generation
models before and after tuning to CityScapes and CamVid respectively.Comment: 9 pages, accepted at CVPR 201
Detecting Functional Requirements Inconsistencies within Multi-teams Projects Framed into a Model-based Web Methodology
One of the most essential processes within the software project life cycle is the REP (Requirements
Engineering Process) because it allows specifying the software product requirements. This specification
should be as consistent as possible because it allows estimating in a suitable manner the effort required to
obtain the final product. REP is complex in itself, but this complexity is greatly increased in big, distributed
and heterogeneous projects with multiple analyst teams and high integration between functional modules.
This paper presents an approach for the systematic conciliation of functional requirements in big projects
dealing with a web model-based approach and how this approach may be implemented in the context of the
NDT (Navigational Development Techniques): a web methodology. This paper also describes the empirical
evaluation in the CALIPSOneo project by analyzing the improvements obtained with our approach.Ministerio de EconomĂa y Competitividad TIN2013-46928-C3-3-RMinisterio de EconomĂa y Competitividad TIN2015-71938-RED
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