Mining Exceptional Social Behaviour

Essentially, our lives are made of social interactions. These can be recorded through personal gadgets as well as sensors adequately attached to people for research purposes. In particular, such sensors may record real time location of people. This location data can then be used to infer interactions, which may be translated into behavioural patterns. In this paper, we focus on the automatic discovery of exceptional social behaviour from spatio-temporal data. For that, we propose a method for Exceptional Behaviour Discovery (EBD). The proposed method combines Subgroup Discovery and Network Science techniques for finding social behaviour that deviates from the norm. In particular, it transforms movement and demographic data into attributed social interaction networks, and returns descriptive subgroups. We applied the proposed method on two real datasets containing location data from children playing in the school playground. Our results indicate that this is a valid approach which is able to obtain meaningful knowledge from the data.This work has been partially supported by the German Research Foundation (DFG) project “MODUS” (under grant AT 88/4-1). Furthermore, the research leading to these results has received funding (JG) from ESRC grant ES/N006577/1. This work was financed by the project Kids First, project number 68639

Open-source intelligence em sistemas SIEM

Tese de mestrado, Segurança Informática, Universidade de Lisboa, Faculdade de Ciências, 2015A OSINT é uma interminável fonte de informação valiosa, em qualquer que seja o contexto, no qual exista a necessidade de lidar com ameaças humanas e imprevisíveis. A segurança informática não é excepção a esta regra e o uso de informação proveniente de canais OSINT tem-se, como temos vindo a observar com o advento da Threat Intelligence, firmado como um componente fundamental. Propomo-nos, com este trabalho, a integrar este canal de valioso conhecimento no SIEM (um paradigma também indispensável da área) de uma forma automatizada, através de uma ferramenta/framework que visa estabelecer a fundação de um instrumento extensível para recolher e reduzir grandes quantidades de informação a conjuntos, utilizáveis e úteis, de valiosos dados e conhecimentos sobre ameaças. Essa ferramenta irá recolher dados e, servindo-se de uma técnica simplista de aprendizagem de máquinas supervisionada, refiná-los, garantindo que ao SIEM apenas é passada informação relevante. Por forma a validar os nossos esforços, providenciamos provas empíricas da aplicabilidade da nossa solução, em contexto prático e real, demonstrando, efectivamente, o poder de síntese, com base em feedback do utilizador, da nossa solução. Os nossos resultados apresentam bons indicadores de que a nossa abordagem é viável e que o nosso componente é capaz de reduzir e filtrar volumes significativos de informação de redes sociais a conjuntos, manuseáveis, de informação estratégica.OSINT is a source of endlessly valuable information for all contexts that have to deal with unpredictable human threats. Computer Security is no exception to this idea and the use of OSINT for Threat Intelligence has been widely established as a fundamental component. We propose to integrate this channel of knowledge into a SIEM platform, a widely employed paradigm in the sector. We also aim to do it in an automated fashion, through a tool that tries to lay the groundwork of an extendable instrument to collect and reduce vast amounts of information to usable amounts of threat data. This tool retrieves data and, leveraging a simplistic supervised machine learning technique, refines it ensuring that the SIEM platform is to receive only relevant information. In order to validate our efforts we provide empirical evidence of the applicability of our solution, demonstrating, in practical context, its power for synthesizing information based on user-provided feedback. Our results reveal good evidence that our approach is a viable one and that our prototype is capable of reducing and filtering large volumes of social networking data, to manageable sets of intelligence

Data Mining on Social Interaction Networks

Social media and social networks have already woven themselves into the veryfabric of everyday life. This results in a dramatic increase of social datacapturing various relations between the users and their associated artifacts,both in online networks and the real world using ubiquitous devices. In thiswork, we consider social interaction networks from a data mining perspective -also with a special focus on real-world face-to-face contact networks: Wecombine data mining and social network analysis techniques for examining thenetworks in order to improve our understanding of the data, the modeledbehavior, and its underlying emergent processes. Furthermore, we adapt, extendand apply known predictive data mining algorithms on social interactionnetworks. Additionally, we present novel methods for descriptive data miningfor uncovering and extracting relations and patterns for hypothesis generationand exploration, in order to provide characteristic information about the dataand networks. The presented approaches and methods aim at extracting valuableknowledge for enhancing the understanding of the respective data, and forsupporting the users of the respective systems. We consider data from severalsocial systems, like the social bookmarking system BibSonomy, the socialresource sharing system flickr, and ubiquitous social systems: Specifically, wefocus on data from the social conference guidance system Conferator and thesocial group interaction system MyGroup. This work first gives a shortintroduction into social interaction networks, before we describe severalanalysis results in the context of online social networks and real-worldface-to-face contact networks. Next, we present predictive data mining methods,i.e., for localization, recommendation and link prediction. After that, wepresent novel descriptive data mining methods for mining communities andpatterns

Data Mining on Social Interaction Networks

Social media and social networks have already woven themselves into the very fabric of everyday life. This results in a dramatic increase of social data capturing various relations between the users and their associated artifacts, both in online networks and the real world using ubiquitous devices. In this work, we consider social interaction networks from a data mining perspective - also with a special focus on real-world face-to-face contact networks: We combine data mining and social network analysis techniques for examining the networks in order to improve our understanding of the data, the modeled behavior, and its underlying emergent processes. Furthermore, we adapt, extend and apply known predictive data mining algorithms on social interaction networks. Additionally, we present novel methods for descriptive data mining for uncovering and extracting relations and patterns for hypothesis generation and exploration, in order to provide characteristic information about the data and networks. The presented approaches and methods aim at extracting valuable knowledge for enhancing the understanding of the respective data, and for supporting the users of the respective systems. We consider data from several social systems, like the social bookmarking system BibSonomy, the social resource sharing system flickr, and ubiquitous social systems: Specifically, we focus on data from the social conference guidance system Conferator and the social group interaction system MyGroup. This work first gives a short introduction into social interaction networks, before we describe several analysis results in the context of online social networks and real-world face-to-face contact networks. Next, we present predictive data mining methods, i.e., for localization, recommendation and link prediction. After that, we present novel descriptive data mining methods for mining communities and patterns