3,046 research outputs found

    SHOE:The extraction of hierarchical structure for machine learning of natural language

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    An Insider Misuse Threat Detection and Prediction Language

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    Numerous studies indicate that amongst the various types of security threats, the problem of insider misuse of IT systems can have serious consequences for the health of computing infrastructures. Although incidents of external origin are also dangerous, the insider IT misuse problem is difficult to address for a number of reasons. A fundamental reason that makes the problem mitigation difficult relates to the level of trust legitimate users possess inside the organization. The trust factor makes it difficult to detect threats originating from the actions and credentials of individual users. An equally important difficulty in the process of mitigating insider IT threats is based on the variability of the problem. The nature of Insider IT misuse varies amongst organizations. Hence, the problem of expressing what constitutes a threat, as well as the process of detecting and predicting it are non trivial tasks that add up to the multi- factorial nature of insider IT misuse. This thesis is concerned with the process of systematizing the specification of insider threats, focusing on their system-level detection and prediction. The design of suitable user audit mechanisms and semantics form a Domain Specific Language to detect and predict insider misuse incidents. As a result, the thesis proposes in detail ways to construct standardized descriptions (signatures) of insider threat incidents, as means of aiding researchers and IT system experts mitigate the problem of insider IT misuse. The produced audit engine (LUARM – Logging User Actions in Relational Mode) and the Insider Threat Prediction and Specification Language (ITPSL) are two utilities that can be added to the IT insider misuse mitigation arsenal. LUARM is a novel audit engine designed specifically to address the needs of monitoring insider actions. These needs cannot be met by traditional open source audit utilities. ITPSL is an XML based markup that can standardize the description of incidents and threats and thus make use of the LUARM audit data. Its novelty lies on the fact that it can be used to detect as well as predict instances of threats, a task that has not been achieved to this date by a domain specific language to address threats. The research project evaluated the produced language using a cyber-misuse experiment approach derived from real world misuse incident data. The results of the experiment showed that the ITPSL and its associated audit engine LUARM provide a good foundation for insider threat specification and prediction. Some language deficiencies relate to the fact that the insider threat specification process requires a good knowledge of the software applications used in a computer system. As the language is easily expandable, future developments to improve the language towards this direction are suggested

    The Concept of a Smart Action – Results from Analyzing Information Systems Literature

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    In recent years, the term \u27smartness\u27 has entered widespread use in research and daily life. It has emerged with various applications of the Internet of Things, such as smart homes and smart factories. However, rapid technological development and careless use of the term mean that, in information systems (IS) research, a common understanding of smartness has not yet been established. And while it is recognized that smartness encompasses more than the use of impressive information technology applications, a unified conceptualization of how smartness is manifested in IS research is lacking. To this end, we conducted a structured literature review applying techniques from Grounded Theory. We found that smartness occurs through actions, in which smart things and individuals interact, process information, and make data-based decisions that are perceived as smart. Building on these findings, we propose the concept of a \u27smart action\u27 and derive a general definition of smartness. Our findings augment knowledge about how smartness is formed, offering a new perspective on smartness. The concept of a smart action unifies and increases understanding of \u27smartness\u27 in IS research. It supports further research by providing a concept for describing, analyzing, and designing smart actions, smart devices, and smart services

    Semantic discovery and reuse of business process patterns

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    Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

    Vicious and Virtuous Circles in the Management of Knowledge: The Case of Infosys Technologies

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    A systems perspective is adopted to explore the challenges that organizations face in harnessing knowledge. A systems perspective draws attention to mutually causal processes that have the potential to generate both vicious and virtuous circles. Based on a longitudinal study at Infosys Technologies, we conclude that knowledge management involves more than just the sponsorship of initiatives at and across different organizational levels. It also involves an active process of steering around and out of vicious circles that will inevitably emerge

    ICSEA 2022: the seventeenth international conference on software engineering advances

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    The Seventeenth International Conference on Software Engineering Advances (ICSEA 2022), held between October 16th and October 20th, 2022, continued a series of events covering a broad spectrum of software-related topics. The conference covered fundamentals on designing, implementing, testing, validating and maintaining various kinds of software. Several tracks were proposed to treat the topics from theory to practice, in terms of methodologies, design, implementation, testing, use cases, tools, and lessons learned. The conference topics covered classical and advanced methodologies, open source, agile software, as well as software deployment and software economics and education. Other advanced aspects are related to on-time practical aspects, such as run-time vulnerability checking, rejuvenation process, updates partial or temporary feature deprecation, software deployment and configuration, and on-line software updates. These aspects trigger implications related to patenting, licensing, engineering education, new ways for software adoption and improvement, and ultimately, to software knowledge management. There are many advanced applications requiring robust, safe, and secure software: disaster recovery applications, vehicular systems, biomedical-related software, biometrics related software, mission critical software, E-health related software, crisis-situation software. These applications require appropriate software engineering techniques, metrics and formalisms, such as, software reuse, appropriate software quality metrics, composition and integration, consistency checking, model checking, provers and reasoning. The nature of research in software varies slightly with the specific discipline researchers work in, yet there is much common ground and room for a sharing of best practice, frameworks, tools, languages and methodologies. Despite the number of experts we have available, little work is done at the meta level, that is examining how we go about our research, and how this process can be improved. There are questions related to the choice of programming language, IDEs and documentation styles and standard. Reuse can be of great benefit to research projects yet reuse of prior research projects introduces special problems that need to be mitigated. The research environment is a mix of creativity and systematic approach which leads to a creative tension that needs to be managed or at least monitored. Much of the coding in any university is undertaken by research students or young researchers. Issues of skills training, development and quality control can have significant effects on an entire department. In an industrial research setting, the environment is not quite that of industry as a whole, nor does it follow the pattern set by the university. The unique approaches and issues of industrial research may hold lessons for researchers in other domains. We take here the opportunity to warmly thank all the members of the ICSEA 2022 technical program committee, as well as all the reviewers. The creation of such a high-quality conference program would not have been possible without their involvement. We also kindly thank all the authors who dedicated much of their time and effort to contribute to ICSEA 2022. We truly believe that, thanks to all these efforts, the final conference program consisted of top-quality contributions. We also thank the members of the ICSEA 2022 organizing committee for their help in handling the logistics of this event. We hope that ICSEA 2022 was a successful international forum for the exchange of ideas and results between academia and industry and for the promotion of progress in software engineering advances
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