Automated ubiquitos delivery of generalised services in a open market

University of Technology, Sydney. Faculty of Information Technology.Telecommunications networks, and the services delivered over those networks have become an integral part of most people's lives in the developed world. The range and availability of these services is increasing, however the management of services still lags well behind technical capability, providing unnecessary barriers to the adoption of available technology. The work described in this dissertation has a primary goal of enabling flexible, automated delivery of any telecommunication-based service. More specifically, a mechanism to solve the administrative problems in enabling end users to automatically establish service agreements for any available service, from any available provider. The aims of this work are to: 1. enable the description of service level agreements(SLA) for generalised telecommunication-based services, and 2. provide mechanisms by which those service level agreements may be managed. The term “generalised services” means that all service types are managed using a common framework and set of processes. To derive at a suitable service level agreement description language, the characteristics of telecommunication-based services are first analysed, along with considerations in delivering a service, including service quality, resource allocation and configuration, service pricing and service ubiquity. The current art in SLA description is studied and the requirements of an appropriate language are proposed. An ontological approach to SLA description is adopted, and an SLA description language is developed based on semantic web technologies. To develop the mechanisms for SLA management, the current art is first analysed, and a set of requirements for a suitable SLA management framework are proposed. These requirements are used to guide the design of a multi-agent SLA negotiation framework, including a detailed description of the communication model, framework processes, and social behaviour of the agents involved. Finally, the SLA description language and the negotiation framework are compared with the closest art, and are assessed against tightly argued criteria. An experimental framework and use cases are developed to explore an application of the proposed solution, and to validate completeness. The approach taken has led to the following two key contributions: 1. A set of formal ontologies that may be used to semantically describe secure service level agreements for any application domain. 2. A multi-agent system providing an open market where services can be discovered, participants identified, and negotiation performed using context specific mechanisms. The conclusions of the work are that an ontology-based SLA description language is appropriate for describing generalised SLAs, and that a distributed, agent based negotiation platform that is based on an open market and uses a minimal set of core processes with an extensible, ontology based communication mechanism is appropriate for managing service level agreements in a generalised, automated and ubiquitous way

DynED: Dynamic Ensemble Diversification in Data Stream Classification

Ensemble methods are commonly used in classification due to their remarkable performance. Achieving high accuracy in a data stream environment is a challenging task considering disruptive changes in the data distribution, also known as concept drift. A greater diversity of ensemble components is known to enhance prediction accuracy in such settings. Despite the diversity of components within an ensemble, not all contribute as expected to its overall performance. This necessitates a method for selecting components that exhibit high performance and diversity. We present a novel ensemble construction and maintenance approach based on MMR (Maximal Marginal Relevance) that dynamically combines the diversity and prediction accuracy of components during the process of structuring an ensemble. The experimental results on both four real and 11 synthetic datasets demonstrate that the proposed approach (DynED) provides a higher average mean accuracy compared to the five state-of-the-art baselines.Comment: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM '23), October 21--25, 2023, Birmingham, United Kingdo

LL(O)D and NLP perspectives on semantic change for humanities research

CC BY 4.0This paper presents an overview of the LL(O)D and NLP methods, tools and data for detecting and representing semantic change, with its main application in humanities research. The paper’s aim is to provide the starting point for the construction of a workflow and set of multilingual diachronic ontologies within the humanities use case of the COST Action Nexus Linguarum, European network for Web-centred linguistic data science, CA18209. The survey focuses on the essential aspects needed to understand the current trends and to build applications in this area of study

Crowd-based Information Organization: A Case Study of the Folk Ontologies in Wikipedia and TV Tropes

Folk ontologies are the organically created, crowd-managed ontology-like structures used to organize information in wikis. This paper takes as case studies the folk ontologies of the wikis Wikipedia and TV Tropes, performing a three-part analysis on them. The first part describes the folk ontologies and examines how they are maintained and used. The second part creates an evaluation framework based on the ontology evaluation literature and considers the folk ontologies in that light. The third part takes a random sample of pages from the wiki and performs a quantitative analysis looking at where and how the folk ontologies appear and what effects their presence has. The goal of the paper is to advance our understanding of how people, in aggregate, organize information.Master of Science in Library Scienc

Need for Speed : analysis of brazilian malware classifiers' expiration date

Orientador : André Ricardo Abed GrégioCoorientador : David Menotti GomesDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 27/02/2018Inclui referênciasResumo: Novos programas maliciosos são criados e liberados diariamente para enganar usuários e superar soluções de segurança, assim exigindo melhora continua nestes mecanismos (por exemplo, atualização constante de antivírus). Apesar da maioria dos programas maliciosos serem "genéricos suficiente para infectar o mesmo tipo de sistema operacional mundialmente, alguns deles estão relacionados as especificidades de um ciberespaço de certos países alvos. Neste trabalho, nos apresentemos uma analise de milhares de exemplares de malware coletados no ciberespaço brasileiro ao longo de vários anos, incluindo suas evoluções e o impacto dessas evoluções na classificação de malware. Nos também disponibilizamos um dataset desse conjunto de malware para permitir que outros experimentos e comparações sejam feitas pela comunidade. Este dataset representa o ciberespaço brasileiro e contem perfis de programas que sao conhecidamente malignos e benignos, baseados em características estáticas de seus binários. Nossa analise utilizou algoritmos de aprendizado de maquina (em particular, nos avaliamos quatro algoritmos populares off-the-shelf : Support Vector Machines, Multilayer Perceptron, KNN e Random Forest) para classificar os programas do nosso dataset como maligno ou benigno (incluindo experimentos com thresholds) e identificar o potencial concept drift que ocorre quando o modelo de classificação evolui com o passar do tempo. Nos também providenciamos detalhes extensos sobre nosso dataset, que e composto por 38.000 programas - 20.000 rotulados como malignos, coletados de anexos de e-mails maliciosos/usuários infectados (coletados em ambos os casos por uma grande instituição financeira brasileira com uma rede distribuída em todo o pais entre 2013 e começo de 2017. Por uma questão de reprodutibilidade e comparação imparcial, nos disponibilizamos publicamente os vetores de características utilizados. Finalmente, nos discutimos os experimentos conduzimos, cuja analise evidencia a existência de concept drift nos programas, tanto benignos como malignos, e mostra que não e possível dizer que existe sasonalidade em nosso dataset. Palavras-chave: Classificação de programas, Identificação de malware, Aprendizado de maquina, Concept drift.Abstract: New malware variants are produced and released daily to deceive users and overcome defense solutions, thus demanding continuous improvements on these mechanisms (e.g., antiviruses constant updates). Although most malware samples are usually "generic" enough to infect the same type of operating system world-widely, some of them are tied to the specificities regarding the cyberspace of certain target countries. In this work, we present an analysis of thousands of malware samples collected in the Brazilian cyberspace along several years, including their evolution and the impact of this evolution on malware classification. We also share a labeled dataset of this Brazilian malware set to allow other experiments and comparisons by the community. This dataset is representative of the Brazilian cyberspace and contains profiles of known-bad and known-good programs based on binaries' static features. Our analysis leveraged machine learning algorithms (in particular, we evaluated four popular off-the-shelf classifiers: Support Vector Machines, Multilayer Perceptron, KNN and Random Forest) to classify the programs of our dataset as malware or goodware (including experiments with thresholds) and to identify the potential concept drift that occurs when the subject of a classification scheme evolves as time goes by. We also provide extensive details about our dataset, which is composed of 38, 000 programs - 20, 000 labeled as known malware, collected from malicious email attachments/infected users (triaged in both cases by a major Brazilian financial institution with a country-wide distributed network) between 2013 and early 2017. For the sake of reproducibility and unbiased comparison, we make the feature vectors produced from our database publicly available. Finally, we discuss the results of the conducted experiments, whose analysis evidences the existence of concept drift on programs, either goodware and malware, and shows that it is not possible to say that there is seasonality in our dataset. Keywords: Program classification, Malware identification, Machine learning, Concept drift