Young people, child pornography, and subcultural norms on the Internet
- Authors: Prichard, Jeremy , Spiranovic, Caroline , Watters, Paul , Lueg, Christopher
- Date: 2013
- Type: Text , Journal article
- Relation: Journal of the American Society for Information Science and Technology Vol. 64, no. 5 (2013), p. 992-1000
- Full Text: false
- Reviewed:
- Description: Literature to date has treated as distinct two issues (a) the influence of pornography on young people and (b) the growth of Internet child pornography, also called child exploitation material (CEM). This article discusses how young people might interact with, and be affected by, CEM. The article first considers the effect of CEM on young victims abused to generate the material. It then explains the paucity of data regarding the prevalence with which young people view CEM online, inadvertently or deliberately. New analyses are presented from a 2010 study of search terms entered on an internationally popular peer-to-peer website, isoHunt. Over 91 days, 162 persistent search terms were recorded. Most of these related to file sharing of popular movies, music, and so forth. Thirty-six search terms were categorized as specific to a youth market and perhaps a child market. Additionally, 4 deviant, and persistent search terms were found, 3 relating to CEM and the fourth to bestiality. The article discusses whether the existence of CEM on a mainstream website, combined with online subcultural influences, may normalize the material for some youth and increase the risk of onset (first deliberate viewing). Among other things, the article proposes that future research examines the relationship between onset and sex offending by youth. © 2013 ASIS&T.
- Description: 2003011027
Application of rank correlation, clustering and classification in information security
- Authors: Beliakov, Gleb , Yearwood, John , Kelarev, Andrei
- Date: 2012
- Type: Text , Journal article
- Relation: Journal of Networks Vol. 7, no. 6 (2012), p. 935-945
- Full Text:
- Reviewed:
- Description: This article is devoted to experimental investigation of a novel application of a clustering technique introduced by the authors recently in order to use robust and stable consensus functions in information security, where it is often necessary to process large data sets and monitor outcomes in real time, as it is required, for example, for intrusion detection. Here we concentrate on a particular case of application to profiling of phishing websites. First, we apply several independent clustering algorithms to a randomized sample of data to obtain independent initial clusterings. Silhouette index is used to determine the number of clusters. Second, rank correlation is used to select a subset of features for dimensionality reduction. We investigate the effectiveness of the Pearson Linear Correlation Coefficient, the Spearman Rank Correlation Coefficient and the Goodman-Kruskal Correlation Coefficient in this application. Third, we use a consensus function to combine independent initial clusterings into one consensus clustering. Fourth, we train fast supervised classification algorithms on the resulting consensus clustering in order to enable them to process the whole large data set as well as new data. The precision and recall of classifiers at the final stage of this scheme are critical for effectiveness of the whole procedure. We investigated various combinations of several correlation coefficients, consensus functions, and a variety of supervised classification algorithms. © 2012 Academy Publisher.
- Description: 2003010277