Botnet cluster identification

Botnets are a group of computers infected with a specific sub-set of a malware family and controlled by one individual, called botmaster. This kind of networks are used not only, but also for virtual extorsion, spam campaigns and identity theft. They implement different types of evasion techniques that make it harder for one to group and detect botnet traffic. This thesis introduces one methodology, called CONDENSER, that outputs clusters through a self-organizing map and that identify domain names generated by an unknown pseudo-random seed that is known by the botnet herder(s). Aditionally DNS Crawler is proposed, this system saves historic DNS data for fast-flux and double fastflux detection, and is used to identify live C&Cs IPs used by real botnets. A program, called CHEWER, was developed to automate the calculation of the SVM parameters and features that better perform against the available domain names associated with DGAs. CONDENSER and DNS Crawler were developed with scalability in mind so the detection of fast-flux and double fast-flux networks become faster. We used a SVM for the DGA classififer, selecting a total of 11 attributes and achieving a Precision of 77,9% and a F-Measure of 83,2%. The feature selection method identified the 3 most significant attributes of the total set of attributes. For clustering, a Self-Organizing Map was used on a total of 81 attributes. The conclusions of this thesis were accepted in Botconf through a submited article. Botconf is known conferênce for research, mitigation and discovery of botnets tailled for the industry, where is presented current work and research. This conference is known for having security and anti-virus companies, law enforcement agencies and researchers.

Relação entre as variáveis sociais, económicas e ambientais com o padrão da distribuição espaço-temporal dos casos de Dengue por munícipio no Brasil : de 2008 até 2012, utilizando o SOM

Atualmente, um dos principais desafios que afeta a saúde pública no Brasil é a crescente evolução no número de casos e epidemias provocados pelo vírus da dengue. Não existem estudos suficientes que consigam elucidar quais fatores contribuem para a evolução das epidemias de Dengue. Fatores como condições sanitárias, localização geográfica, investimentos financeiros em infraestrutura e qualidade de vida podem estar relacionados com a incidência de Dengue. Além disso, outra questão que merece um maior destaque é o estudo para se identificar o grau de impacto das variáveis determinantes da dengue e se existe um padrão que está correlacionado com a taxa de incidência. Desta forma, este trabalho tem como objetivo principal a correlação da taxa de incidência da dengue na população de cada município brasileiro, utilizando dados relativos aos aspectos sociais, econômicos, demográficos e ambientais. Outra contribuição relevante do trabalho, foi a análise dos padrões de distribuição espacial da taxa de incidência de Dengue e sua relação com os padrões encontrados utilizando as variáveis socioeconômicas e ambientais, sobretudo analisando a evolução temporal no período de 2008 até 2012. Para essa análises, utilizou-se o Sistema de Informação Geográfica (SIG) aliado com a mineração de dados, através da metodologia de rede neural mais especificamente o mapa auto organizável de Kohonen ou self-organizing maps (SOM). Tal metodologia foi empregada para a identificação de padrão de agrupamentos dessas variáveis e sua relação com as classes de incidência de dengue no Brasil (Alta, Média e Baixa). Assim, este projeto contribui de forma significativa para uma melhor compreensão dos fatores que estão associados à ocorrência de Dengue, e como essa doença está correlacionada com fatores como: meio ambiente, infraestrutura e localização no espaço geográfico.

Geografia eleitoral : análise espacial de vinte anos de resultados eleitorais

A Geografia Eleitoral definida como a análise da interação entre o espaço, o lugar e os processos eleitorais, compreende fundamentalmente três domínios: padrões de voto, influências geográficas nas eleições e a geografia da representação. A Geografia Eleitoral conta uma longa história, ao ponto de já ter tido um status próprio no âmbito da disciplina. Depois ter aparecido nos anos 70 e 80 com algum vigor no contexto português, esta abordagem dos fenómenos eleitorais tem sido relativamente negligenciada nos anos. Neste trabalho, conjugando as metodologias espaciais-analíticas mais tradicionais com um conjunto de novas tecnologias - como os Sistemas de Informação Geográfica (SIG) e os Self- Organizing Maps (SOM) -, pretendemos dar uma nova ênfase à Geografia Eleitoral nacional, realçando o seu caráter explicativo e abrindo portas a abordagens multidisciplinares dos dados eleitorais. Com base nos resultados das eleições legislativas portuguesas realizadas no período compreendido entre 1991 e 2011, analisamos neste trabalho os seguintes tópicos: a distribuição espacial dos resultados, em conjunto e individualmente, dos cinco partidos com representação parlamentar; a distribuição dos resultados deste conjunto de partidos por região, considerando uma das propostas de divisão administrativa referendada em 1998 e analisando a região da Estremadura e Ribatejo como um estudo de caso; os padrões gerados pela distribuição do bloco constituído pelos dois principais partidos (PS e PPD/PSD); o comportamento espacial dos blocos Direita/Centro-Direita e Esquerda/Centro- Esquerda; a abstenção eleitoral, confrontando os valores registados em cada freguesia com o resultado nacional; a comparação entre diferentes tipos de eleições; a distribuição dos resultados por partido nos dois principais distritos (Lisboa e Porto) que em conjunto representam mais de 40% da população portuguesa; o comportamento do Bloco de Esquerda, o mais jovem dos partidos considerados; e os mapeamentos das freguesias “sociais-democratas” e “socialistas”. Os resultados deste trabalho comprovam de forma geral, de que a georreferenciação dos dados eleitorais nacionais geram uma cartografia que permite confirmar aquilo que outras análises têm vindo a mostrar sobre o comportamento eleitoral dos portugueses. No entanto, existem aspectos específicos da distribuição espacial deste mesmo comportamento eleitoral que permitem aprofundar o conhecimento sobre a interacção entre o espaço e os processos eleitorais.

Contributions towards smart cities : exploring block level census data for the characterization of change in Lisbon

The interest in using information to improve the quality of living in large urban areas and its governance efficiency has been around for decades. Nevertheless, the improvements in Information and Communications Technology has sparked a new dynamic in academic research, usually under the umbrella term of Smart Cities. This concept of Smart City can probably be translated, in a simplified version, into cities that are lived, managed and developed in an information-saturated environment. While it makes perfect sense and we can easily foresee the benefits of such a concept, presently there are still several significant challenges that need to be tackled before we can materialize this vision. In this work we aim at providing a small contribution in this direction, which maximizes the relevancy of the available information resources. One of the most detailed and geographically relevant information resource available, for the study of cities, is the census, more specifically the data available at block level (Subsecção Estatística). In this work, we use Self-Organizing Maps (SOM) and the variant Geo-SOM to explore the block level data from the Portuguese census of Lisbon city, for the years of 2001 and 2011. We focus on gauging change, proposing ways that allow the comparison of the two time periods, which have two different underlying geographical bases. We proceed with the analysis of the data using different SOM variants, aiming at producing a two-fold portrait: one, of the evolution of Lisbon during the first decade of the XXI century, another, of how the census dataset and SOM’s can be used to produce an informational framework for the study of cities.

Implementación de una metodología docente basada en las TIC y orientada al autoaprendizaje en asignaturas con un elevado contenido iconográfico en la Licenciatura de Veterinària de la UAB

La adaptación de los estudios universitarios al Espacio Europeo de Educación Superior (EEES) pretende conseguir un nuevo modelo educativo basado en el aprendizaje activo del estudiante. En este sentido, las Tecnologías de la Información y la Comunicación (TICs) pueden desempeñar un papel importante en la renovación de la metodología docente, y muy especialmente en asignaturas donde la carga iconográfica es fundamental, tal como ocurre en las Ciencias morfológicas y en algunas materias clínicas. En la Licenciatura en Veterinària de la UAB la carga presencial del alumno es muy elevada, lo que deja poco tiempo para el autoaprendizaje activo y el estudio autónomo. Para intentar paliar este problema, en nuestra Titulación se han elaborado en los últimos años diversos atlas y otros documentos virtuales cuyos contenidos didácticos están relacionados con materias como la Anatomía, Parasitología, Radiología y Anatomía Patológica. Estos materiales, algunos de los cuales ya están publicados on line en la plataforma Veterinària Virtual (http://quiro.uab.es), y que están a disposición de los estudiantes, posibilitan reducir en parte la carga presencial, sirven de ayuda en el proceso de enseñanza y aprendizaje, facilitan el aprendizaje no presencial, autónomo y activo y permiten la evaluación continuada, consiguiendo en definitiva un aumento del protagonismo del alumno en el proceso educativo, lo que constituye una de las metas de la adaptación al EEES. Los alumnos valoran muy positivamente la publicación on line de material educativo, ya que representa un recurso didáctico fácilmente disponible, de acceso permanente y de bajo coste económico. La duración del proyecto ha sido de dos años.

Obtenció de jerarquies d'estats cel·lulars via web

Hi ha diversos mètodes d'anàlisi que duen a terme una agrupació global de la sèries de mostres de microarrays, com SelfOrganizing Maps, o que realitzen agrupaments locals tenint en compte només un subconjunt de gens coexpressats, com Biclustering, entre d'altres. En aquest projecte s'ha desenvolupat una aplicació web: el PCOPSamplecl, és una eina que pertany als mètodes d'agrupació (clustering) local, que no busca subconjunts de gens coexpresats (anàlisi de relacions linials), si no parelles de gens que davant canvis fenotípics, la seva relació d'expressió pateix fluctuacions. El resultats del PCOPSamplecl seràn les diferents distribucions finals de clusters i les parelles de gens involucrades en aquests canvis fenotípics. Aquestes parelles de gens podràn ser estudiades per trobar la causa i efecte del canvi fenotípic. A més, l'eina facilita l'estudi de les dependències entre les diferents distribucions de clusters que proporciona l'aplicació per poder estudiar la intersecció entre clusters o l'aparició de subclusters (2 clusters d'una mateixa agrupació de clusters poden ser subclusters d'altres clusters de diferents distribucions de clusters). L'eina és disponible al servidor: http://revolutionresearch.uab.es/

Research in psychopathology: epistemologic issues.

Etiologic research in psychiatry relies on an objectivist epistemology positing that human cognition is specified by the "reality" of the outer world, which consists of a totality of mind-independent objects. Truth is considered as some sort of correspondence relation between words and external objects, and mind as a mirror of nature. In our view, this epistemology considerably impedes etiologic research. Objectivist epistemology has been recently confronting a growing critique from diverse scientific fields. Alternative models in neurosciences (neuronal selection), artificial intelligence (connectionism), and developmental psychology (developmental biodynamics) converge in viewing living organisms as self-organizing systems. In this perspective, the organism is not specified by the outer world, but enacts its environment by selecting relevant domains of significance that constitute its world. The distinction between mind and body or organism and environment is a matter of observational perspective. These models from empirical sciences are compatible with fundamental tenets of philosophical phenomenology and hermeneutics. They imply consequences for research in psychopathology: symptoms cannot be viewed as disconnected manifestations of discrete localized brain dysfunctions. Psychopathology should therefore focus on how the person's self-coherence is maintained and on the understanding and empirical investigation of the systemic laws that govern neurodevelopment and the organization of human cognition.

Myosin Vs organize actin cables in fission yeast.

Myosin V motors are believed to contribute to cell polarization by carrying cargoes along actin tracks. In Schizosaccharomyces pombe, Myosin Vs transport secretory vesicles along actin cables, which are dynamic actin bundles assembled by the formin For3 at cell poles. How these flexible structures are able to extend longitudinally in the cell through the dense cytoplasm is unknown. Here we show that in myosin V (myo52 myo51) null cells, actin cables are curled, bundled, and fail to extend into the cell interior. They also exhibit reduced retrograde flow, suggesting that formin-mediated actin assembly is impaired. Myo52 may contribute to actin cable organization by delivering actin regulators to cell poles, as myoV defects are partially suppressed by diverting cargoes toward cell tips onto microtubules with a kinesin 7-Myo52 tail chimera. In addition, Myo52 motor activity may pull on cables to provide the tension necessary for their extension and efficient assembly, as artificially tethering actin cables to the nuclear envelope via a Myo52 motor domain restores actin cable extension and retrograde flow in myoV mutants. Together these in vivo data reveal elements of a self-organizing system in which the motors shape their own tracks by transporting cargoes and exerting physical pulling forces.

Advance directives based cognitive therapy in bipolar disorder

Background and Aims: Mental Health Advance Directives (ADs) are potentially useful for bipolar patients due to the episodic characteristic of their disease. An advanced directives based cognitive therapy (ADCBT) involving the self-determination model for adherence, the cognitive representation of illness model, and the concordance model is studied on this article. The aim of the study is to evaluate ADBCT's impact on the number and duration of hospitalization as well as commitment and seclusion procedures. Methods: Charts of all patients who have written their ADs following an ADBCT intervention since at least 24 months were included in the study. Number and duration of psychiatric hospitalization for a mood or a psychotic episode as well as commitment and seclusion procedures were recorded for each patient two years before ADBCT and during a follow up of at least 24 months. Results: Number of hospitalizations, number of commitment procedures and number of days spent in psychiatric hospital reduced significantly after ADCBT in comparison of the two years who preceded the intervention. Conclusions: ADBCT seems to be effective in patients with compliance and coercion problems in this retrospective study. Its effect remains however to be confirmed in large prospective studies.

The Tea4-PP1 landmark promotes local growth by dual Cdc42 GEF recruitment and GAP exclusion.

Cell polarization relies on small GTPases, such as Cdc42, which can break symmetry through self-organizing principles, and landmarks that define the axis of polarity. In fission yeast, microtubules deliver the Tea1-Tea4 complex to mark cell poles for growth, but how this complex activates Cdc42 is unknown. Here, we show that ectopic targeting of Tea4 to cell sides promotes the local activation of Cdc42 and cell growth. This activity requires that Tea4 binds the type I phosphatase (PP1) catalytic subunit Dis2 or Sds21, and ectopic targeting of either catalytic subunit is similarly instructive for growth. The Cdc42 guanine-nucleotide-exchange factor Gef1 and the GTPase-activating protein Rga4 are required for Tea4-PP1-dependent ectopic growth. Gef1 is recruited to ectopic Tea4 and Dis2 locations to promote Cdc42 activation. By contrast, Rga4 is locally excluded by Tea4, and its forced colocalization with Tea4 blocks ectopic growth, indicating that Rga4 must be present, but at sites distinct from Tea4. Thus, a Tea4-PP1 landmark promotes local Cdc42 activation and growth both through Cdc42 GEF recruitment and by creating a local trough in a Cdc42 GAP.

Bayesian image reconstruction with space-variant noise suppression

In this paper we present a Bayesian image reconstruction algorithm with entropy prior (FMAPE) that uses a space-variant hyperparameter. The spatial variation of the hyperparameter allows different degrees of resolution in areas of different statistical characteristics, thus avoiding the large residuals resulting from algorithms that use a constant hyperparameter. In the first implementation of the algorithm, we begin by segmenting a Maximum Likelihood Estimator (MLE) reconstruction. The segmentation method is based on using a wavelet decomposition and a self-organizing neural network. The result is a predetermined number of extended regions plus a small region for each star or bright object. To assign a different value of the hyperparameter to each extended region and star, we use either feasibility tests or cross-validation methods. Once the set of hyperparameters is obtained, we carried out the final Bayesian reconstruction, leading to a reconstruction with decreased bias and excellent visual characteristics. The method has been applied to data from the non-refurbished Hubble Space Telescope. The method can be also applied to ground-based images.

BIO-INSPIRED COMPUTATIONAL TECHNIQUES APPLIED TO THE CLUSTERING AND VISUALIZATION OF SPATIO-TEMPORAL GEOSPATIAL DATA

The coverage and volume of geo-referenced datasets are extensive and incessantly¦growing. The systematic capture of geo-referenced information generates large volumes¦of spatio-temporal data to be analyzed. Clustering and visualization play a key¦role in the exploratory data analysis and the extraction of knowledge embedded in¦these data. However, new challenges in visualization and clustering are posed when¦dealing with the special characteristics of this data. For instance, its complex structures,¦large quantity of samples, variables involved in a temporal context, high dimensionality¦and large variability in cluster shapes.¦The central aim of my thesis is to propose new algorithms and methodologies for¦clustering and visualization, in order to assist the knowledge extraction from spatiotemporal¦geo-referenced data, thus improving making decision processes.¦I present two original algorithms, one for clustering: the Fuzzy Growing Hierarchical¦Self-Organizing Networks (FGHSON), and the second for exploratory visual data analysis:¦the Tree-structured Self-organizing Maps Component Planes. In addition, I present¦methodologies that combined with FGHSON and the Tree-structured SOM Component¦Planes allow the integration of space and time seamlessly and simultaneously in¦order to extract knowledge embedded in a temporal context.¦The originality of the FGHSON lies in its capability to reflect the underlying structure¦of a dataset in a hierarchical fuzzy way. A hierarchical fuzzy representation of¦clusters is crucial when data include complex structures with large variability of cluster¦shapes, variances, densities and number of clusters. The most important characteristics¦of the FGHSON include: (1) It does not require an a-priori setup of the number¦of clusters. (2) The algorithm executes several self-organizing processes in parallel.¦Hence, when dealing with large datasets the processes can be distributed reducing the¦computational cost. (3) Only three parameters are necessary to set up the algorithm.¦In the case of the Tree-structured SOM Component Planes, the novelty of this algorithm¦lies in its ability to create a structure that allows the visual exploratory data analysis¦of large high-dimensional datasets. This algorithm creates a hierarchical structure¦of Self-Organizing Map Component Planes, arranging similar variables' projections in¦the same branches of the tree. Hence, similarities on variables' behavior can be easily¦detected (e.g. local correlations, maximal and minimal values and outliers).¦Both FGHSON and the Tree-structured SOM Component Planes were applied in¦several agroecological problems proving to be very efficient in the exploratory analysis¦and clustering of spatio-temporal datasets.¦In this thesis I also tested three soft competitive learning algorithms. Two of them¦well-known non supervised soft competitive algorithms, namely the Self-Organizing¦Maps (SOMs) and the Growing Hierarchical Self-Organizing Maps (GHSOMs); and the¦third was our original contribution, the FGHSON. Although the algorithms presented¦here have been used in several areas, to my knowledge there is not any work applying¦and comparing the performance of those techniques when dealing with spatiotemporal¦geospatial data, as it is presented in this thesis.¦I propose original methodologies to explore spatio-temporal geo-referenced datasets¦through time. Our approach uses time windows to capture temporal similarities and¦variations by using the FGHSON clustering algorithm. The developed methodologies¦are used in two case studies. In the first, the objective was to find similar agroecozones¦through time and in the second one it was to find similar environmental patterns¦shifted in time.¦Several results presented in this thesis have led to new contributions to agroecological¦knowledge, for instance, in sugar cane, and blackberry production.¦Finally, in the framework of this thesis we developed several software tools: (1)¦a Matlab toolbox that implements the FGHSON algorithm, and (2) a program called¦BIS (Bio-inspired Identification of Similar agroecozones) an interactive graphical user¦interface tool which integrates the FGHSON algorithm with Google Earth in order to¦show zones with similar agroecological characteristics.

Antikaon potential in hot and dense matter

The antikaon optical potential in hot and dense nuclear matter is studied within the framework of a coupled-channel self-consistent calculation taking, as bare meson-baryon interaction, the meson-exchange potential of the Jlich group. Typical conditions found in heavy-ion collisions at GSI are explored. As in the case of zero temperature, the angular momentum components larger than L=0 contribute significantly to the finite temperature antikaon optical potential at finite momentum. It is found that the particular treatment of the medium effects has a strong influence on the behavior of the antikaon potential with temperature. Our self-consistent model, in which antikaons and pions are dressed in the medium, gives a moderately temperature dependent antikaon potential which remains attractive at GSI temperatures, contrary to what one finds if only nuclear Pauli blocking effects are included.

Identification of different heart tissues from MRI C-SENC images using an unsupervised multi-stage fuzzy clustering technique.

PURPOSE: To objectively characterize different heart tissues from functional and viability images provided by composite-strain-encoding (C-SENC) MRI. MATERIALS AND METHODS: C-SENC is a new MRI technique for simultaneously acquiring cardiac functional and viability images. In this work, an unsupervised multi-stage fuzzy clustering method is proposed to identify different heart tissues in the C-SENC images. The method is based on sequential application of the fuzzy c-means (FCM) and iterative self-organizing data (ISODATA) clustering algorithms. The proposed method is tested on simulated heart images and on images from nine patients with and without myocardial infarction (MI). The resulting clustered images are compared with MRI delayed-enhancement (DE) viability images for determining MI. Also, Bland-Altman analysis is conducted between the two methods. RESULTS: Normal myocardium, infarcted myocardium, and blood are correctly identified using the proposed method. The clustered images correctly identified 90 +/- 4% of the pixels defined as infarct in the DE images. In addition, 89 +/- 5% of the pixels defined as infarct in the clustered images were also defined as infarct in DE images. The Bland-Altman results show no bias between the two methods in identifying MI. CONCLUSION: The proposed technique allows for objectively identifying divergent heart tissues, which would be potentially important for clinical decision-making in patients with MI.

Waste management policies for industrial symbiosis development: case studies in European countries

Industrial symbiosis (IS) emerged as a self-organizing business strategy among firms that are willing to cooperate to improve their economic and environmental performance. The adoption of such cooperative strategies relates to increasing costs of waste management, most of which are driven by policy and legislative requirements. Development of IS depends on an enabling context of social, informational, technological, economical and political factors. The power to influence this context varies among the agents involved such as the government, businesses or coordinating entities. Governmental intervention, as manifested through policies, could influence a wider range of factors; and we believe this is an area which is under-researched. This paper aims to critically appraise the waste policy interventions from supra-national to sub-national levels of government. A case study methodology has been applied to four European countries i.e. Denmark, the UK, Portugal and Switzerland, in which IS emerged or is being fostered. The findings suggest that there are commonalities in policy instruments that may have led to an IS enabling context. The paper concludes with lessons learnt and recommendations on shaping the policy context for IS development.