Symbolic Computing with Incremental Mindmaps to Manage and Mine Data Streams - Some Applications

In our understanding, a mind-map is an adaptive engine that basically works incrementally on the fundament of existing transactional streams. Generally, mind-maps consist of symbolic cells that are connected with each other and that become either stronger or weaker depending on the transactional stream. Based on the underlying biologic principle, these symbolic cells and their connections as well may adaptively survive or die, forming different cell agglomerates of arbitrary size. In this work, we intend to prove mind-maps' eligibility following diverse application scenarios, for example being an underlying management system to represent normal and abnormal traffic behaviour in computer networks, supporting the detection of the user behaviour within search engines, or being a hidden communication layer for natural language interaction.Comment: 4 pages; 4 figure

Inner-Eye: Appearance-based Detection of Computer Scams

As more and more inexperienced users gain Internet access, fraudsters are attempting to take advantage of them in new ways. Instead of sophisticated exploitation techniques, simple confidence tricks can be used to create malware that is both very effective and likely to evade detection by traditional security software. Heuristics that detect complex malicious behavior are powerless against some common frauds. This work explores the use of imaging and text-matching techniques to detect typical computer scams such as pharmacy and rogue antivirus frauds. The Inner-Eye system implements the chosen approach in a scalable and efficient manner through the use of virtualization

Marketing Privacy: A Solution for the Blight of Telemarketing (and Spam and Junk Mail)

Unsolicited solicitations in the form of telemarketing calls, email spam and junk mail impose in aggregate a substantial negative externality on society. Telemarketers do not bear the full costs of their marketing because they do not compensate recipients for the hassle of, say, being interrupted during dinner. Current regulatory responses that give consumers the all-or-nothing option of registering on the Internet to block all unsolicited telemarketing calls are needlessly both over- and underinclusive. A better solution is to allow individual consumers to choose the price per minute they would like to receive as compensation for listening to telemarketing calls. Such a name your own price mechanism could be easily implemented technologically by crediting consumers\u27 phone bills (a method analogous to the current debits to bills from 1-900 calls). Compensated calling is also easily implemented within current don\u27t call statutes simply by giving don\u27t-call households the option to authorize intermediaries to connect calls that meet their particular manner or compensation prerequisites. Under this rule, consumers are presumptively made better off by a regime that gives them greater freedom. Telemarketing firms facing higher costs of communication are likely to better screen potential contacts. Consumers having the option of choosing an intermediate price will receive fewer calls, which will be better tailored to their interests, and will be compensated for those calls they do receive. Giving consumers the right to be compensated may also benefit some telemarketers. Once consumers are voluntarily opting to receive telemarketing calls (in return for tailored compensation), it becomes possible to deregulate the telemarketers—lifting current restrictions on the time (no night time calls) and manner (no recorded calls). And faced with increasing caller resistance, we imagine that survey groups, such as the Gallop Poll, might welcome the opportunity to compensate survey respondents so that they might be able to produce more representative samples

Deteção de propagação de ameaças e exfiltração de dados em redes empresariais

Modern corporations face nowadays multiple threats within their networks. In an era where companies are tightly dependent on information, these threats can seriously compromise the safety and integrity of sensitive data. Unauthorized access and illicit programs comprise a way of penetrating the corporate networks, able to traversing and propagating to other terminals across the private network, in search of confidential data and business secrets. The efficiency of traditional security defenses are being questioned with the number of data breaches occurred nowadays, being essential the development of new active monitoring systems with artificial intelligence capable to achieve almost perfect detection in very short time frames. However, network monitoring and storage of network activity records are restricted and limited by legal laws and privacy strategies, like encryption, aiming to protect the confidentiality of private parties. This dissertation proposes methodologies to infer behavior patterns and disclose anomalies from network traffic analysis, detecting slight variations compared with the normal profile. Bounded by network OSI layers 1 to 4, raw data are modeled in features, representing network observations, and posteriorly, processed by machine learning algorithms to classify network activity. Assuming the inevitability of a network terminal to be compromised, this work comprises two scenarios: a self-spreading force that propagates over internal network and a data exfiltration charge which dispatch confidential info to the public network. Although features and modeling processes have been tested for these two cases, it is a generic operation that can be used in more complex scenarios as well as in different domains. The last chapter describes the proof of concept scenario and how data was generated, along with some evaluation metrics to perceive the model’s performance. The tests manifested promising results, ranging from 96% to 99% for the propagation case and 86% to 97% regarding data exfiltration.Nos dias de hoje, várias organizações enfrentam múltiplas ameaças no interior da sua rede. Numa época onde as empresas dependem cada vez mais da informação, estas ameaças podem compremeter seriamente a segurança e a integridade de dados confidenciais. O acesso não autorizado e o uso de programas ilícitos constituem uma forma de penetrar e ultrapassar as barreiras organizacionais, sendo capazes de propagarem-se para outros terminais presentes no interior da rede privada com o intuito de atingir dados confidenciais e segredos comerciais. A eficiência da segurança oferecida pelos sistemas de defesa tradicionais está a ser posta em causa devido ao elevado número de ataques de divulgação de dados sofridos pelas empresas. Desta forma, o desenvolvimento de novos sistemas de monitorização ativos usando inteligência artificial é crucial na medida de atingir uma deteção mais precisa em curtos períodos de tempo. No entanto, a monitorização e o armazenamento dos registos da atividade da rede são restritos e limitados por questões legais e estratégias de privacidade, como a cifra dos dados, visando proteger a confidencialidade das entidades. Esta dissertação propõe metodologias para inferir padrões de comportamento e revelar anomalias através da análise de tráfego que passa na rede, detetando pequenas variações em comparação com o perfil normal de atividade. Delimitado pelas camadas de rede OSI 1 a 4, os dados em bruto são modelados em features, representando observações de rede e, posteriormente, processados por algoritmos de machine learning para classificar a atividade de rede. Assumindo a inevitabilidade de um terminal ser comprometido, este trabalho compreende dois cenários: um ataque que se auto-propaga sobre a rede interna e uma tentativa de exfiltração de dados que envia informações para a rede pública. Embora os processos de criação de features e de modelação tenham sido testados para estes dois casos, é uma operação genérica que pode ser utilizada em cenários mais complexos, bem como em domínios diferentes. O último capítulo inclui uma prova de conceito e descreve o método de criação dos dados, com a utilização de algumas métricas de avaliação de forma a espelhar a performance do modelo. Os testes mostraram resultados promissores, variando entre 96% e 99% para o caso da propagação e entre 86% e 97% relativamente ao roubo de dados.Mestrado em Engenharia de Computadores e Telemátic

POVERTY LAWGORITHMS A Poverty Lawyer’s Guide to Fighting Automated Decision-Making Harms on Low-Income Communities

Automated decision-making systems make decisions about our lives, and those with low-socioeconomic status often bear the brunt of the harms these systems cause. Poverty Lawgorithms: A Poverty Lawyers Guide to Fighting Automated Decision-Making Harms on Low-Income Communities is a guide by Data & Society Faculty Fellow Michele Gilman to familiarize fellow poverty and civil legal services lawyers with the ins and outs of data-centric and automated-decision making systems, so that they can clearly understand the sources of the problems their clients are facing and effectively advocate on their behalf

Processing spam: Conducting processed listening and rhythmedia to (re)produce people and territories

This thesis provides a transdisciplinary investigation of ‘deviant’ media categories, specifically spam and noise, and the way they are constructed and used to (re)produce territories and people. Spam, I argue, is a media phenomenon that has always existed, and received different names in different times. The changing definitions of spam, the reasons and actors behind these changes are thus the focus of this research. It brings to the forefront a longer history of the politics of knowledge production with and in media, and its consequences. This thesis makes a contribution to the media and communication field by looking at neglected media phenomena through fields such as sound studies, software studies, law and history to have richer understanding that disciplinary boundaries fail to achieve. The thesis looks at three different case studies: the conceptualisation of noise in the early 20th century through Bell Telephone Company, web metric standardisation in the European Union 2000s legislation, and unwanted behaviours on Facebook. What these cases show is that media practitioners have been constructing ‘deviant’ categories in different media and periods by using seven sonic epistemological strategies: training of the (digital) body, restructuring of territories, new experts, standardising measurements (tools and units), filtering, de-politicising and licensing. Informed by my empirical work, I developed two concepts - processed listening and rhythmedia - offering a new theoretical framework to analyse how media practitioners construct power relations by knowing people in mediated territories and then spatially and temporally (re)ordering them. Shifting the attention from theories of vision allows media researchers to have a better understanding of practitioners who work in multi-layered digital/datafied spaces, tuning in and out to continuously measure and record people’s behaviours. Such knowledge is being fed back in a recursive feedback-loop conducted by a particular rhythmedia constantly processing, ordering, shaping and regulating people, objects and spaces. Such actions (re)configure the boundaries of what it means to be human, worker and medium

Search engine bias: the structuration of traffic on the World-Wide Web

Search engines are essential components of the World Wide Web; both commercially and in terms of everyday usage, their importance is hard to overstate. This thesis examines the question of why there is bias in search engine results – bias that invites users to click on links to large websites, commercial websites, websites based in certain countries, and websites written in certain languages. In this thesis, the historical development of the search engine industry is traced. Search engines first emerged as prototypical technological startups emanating from Silicon Valley, followed by the acquisition of search engine companies by major US media corporations and their development into portals. The subsequent development of pay-per-click advertising is central to the current industry structure, an oligarchy of virtually integrated companies managing networks of syndicated advertising and traffic distribution. The study also shows a global landscape in which search production is concentrated in and caters for large global advertising markets, leaving the rest of the world with patchy and uneven search results coverage. The analysis of interviews with senior search engine engineers indicates that issues of quality are addressed in terms of customer service and relevance in their discourse, while the analysis of documents, interviews with search marketers, and participant observation within a search engine marketing firm showed that producers and marketers had complex relationships that combine aspects of collaboration, competition, and indifference. The results of the study offer a basis for the synthesis of insights of the political economy of media and communication and the social studies of technology tradition, emphasising the importance of culture in constructing and maintaining both local structures and wider systems. In the case of search engines, the evidence indicates that the culture of the technological entrepreneur is very effective in creating a new megabusiness, but less successful in encouraging a debate on issues of the public good or public responsibility as they relate to the search engine industry