Welcome to Research Repository UCD
Research Repository UCD is a digital collection of open access scholarly research publications from University College Dublin. Research Repository UCD collects, preserves and makes freely available publications including peer-reviewed articles, working papers and conference papers created by UCD researchers. Where material has already been published it is made available subject to the open-access policies of the original publishers. This service is maintained by UCD Library.
- PublicationFocus groups versus individual interviews with children : A comparison of data(Routledge (Taylor & Francis), 2006)In recent years there has been an increase in the use of qualitative data collection techniques in research with children. Among the most common of these methods are focus groups and individual interviews. While many authors claim that focus groups have advantages over individual interviews, these claims have not been tested empirically with children. The present study reports on the use of focus groups and interviews to collect qualitative data from 116 children in three age groups, with mean ages of 8.4, 11.5 and 14.3 years. The children were randomly allocated to participate in either focus groups or individual interviews where they were presented with identical material and questions relating to their beliefs about peers with psychological disorders. In line with previous research, the interviews produced significantly more relevant and unique ideas about the causes of these disorders than the focus groups, but the latter gave rise to greater elaboration of ideas. The participating children showed no significant difference in their preference for one method over the other. Thus, whether to choose individual interviews or focus groups is likely to depend on the nature of the research question in any given study.
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- PublicationBehavioral Service Graphs: A Big Data Approach for Prompt Investigation of Internet-Wide Infections(IEEE, 2016-11-23)The task of generating network-based evidence to support network forensic investigation is becoming increasingly prominent. Undoubtedly, such evidence is significantly imperative as it not only can be used to diagnose and respond to various network-related issues (i.e., performance bottlenecks, routing issues, etc.) but more importantly, can be leveraged to infer and further investigate network security intrusions and infections. In this context, this paper proposes a proactive approach that aims at generating accurate and actionable network-based evidence related to groups of compromised network machines. The approach is envisioned to guide investigators to promptly pinpoint such malicious groups for possible immediate mitigation as well as empowering network and digital forensic specialists to further examine those machines using auxiliary collected data or extracted digital artifacts. On one hand, the promptness of the approach is successfully achieved by monitoring and correlating perceived probing activities, which are typically the very first signs of an infection or misdemeanors. On the other hand, the generated evidence is accurate as it is based on an anomaly inference that fuses big data behavioral analytics in conjunction with formal graph theoretical concepts. We evaluate the proposed approach as a global capability in a security operations center. The empirical evaluations, which employ 80 GB of real darknet traffic, indeed demonstrates the accuracy, effectiveness and simplicity of the generated network-based evidence.