BazEkon - The Main Library of the Cracow University of Economics

BazEkon home page

Main menu

Author
Węcel Krzysztof (Uniwersytet Ekonomiczny w Poznaniu), Piotrowski Jacek
Title
Metoda oceny stopnia zagrożenia związanego z publikacją ogłoszeń internetowych
The method for scoring of threat related to online adverts publication
Source
Roczniki Kolegium Analiz Ekonomicznych / Szkoła Główna Handlowa, 2013, nr 29, s. 325-341, rys., bibliogr. 18 poz.
Keyword
Portale internetowe, Bezpieczeństwo w internecie
Web portals, Internet security
Note
streszcz., summ.
Abstract
W pracy zaproponowano metodę punktacji ogłoszeń dostępnych w portalach internetowych jako zagrożenie rozumiane jako możliwość łamania prawa. Mamy specjalnie koncentrować się na nielegalnym handlu narkotykami. Połączone wyniki zasady trzech składników są oddzielne: osoba publikuje ofertę, w obrocie dobre i zamierzonego działania (kupno, sprzedaż, inne).(fragment tekstu)

The paper proposes a method for scoring adverts available in online portals as a threat understood as the possibility of breaking the law. We specifically focus on illegal drug trade scenario. The combined score bases on three components scored separately: person publishing the offer, traded good, and intended action (buy, sell, other).(original abstract)
Accessibility
The Main Library of the Cracow University of Economics
The Library of Warsaw School of Economics
The Main Library of Poznań University of Economics and Business
Bibliography
Show
  1. Arrow K.J., Social Choice and Individual Values, Yale University Press, 1951.
  2. Artigas-Fuentes F., Fast k-NN classifier for documents based on a graph structure, "Progress in Pattern Recognition, Image Analysis, Computer Vision, and Aplication" 2010, vol. 6419, s. 228-235, http://www.springerlink. com/mdex/M3177401066H2337.pdf.
  3. Brutlag J.D., Meek C., Challenges of the Email Domain for Text Classification, w: ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning, 2000, s. 103-110, http://dl.acm.org/citation.cfm?id=645529.657817.
  4. Chang M., Poon C.K., Using phrases as features in email classification, "Journal of Systems and Software" 2009, vol. 82(6), s. 1036-1045, doi:10.1016/j. jss.2009.01.013.
  5. Chinavle D., Kolari P., Ensembles in adversarial classification for spam, w: Proceedings of the 18th ACM conference on Information and knowledge management, 2009, s. 2015-2018, http://dl.acm.org/citation.cfm?id= 1646290.
  6. Elo A., The Rating of Chessplayers, Past and Present, Arco 1978.
  7. Gee K., Using latent semantic indexing to filter spam, w: Proceedings of the 2003 ACM symposium on Applied computing, 2003, s. 460-464, http://dl.acm.org/cita- tion.cfm?id=952623.
  8. Karras D., An improved text categorization methodology based on second and third order probabilistic feature extraction and neural network classifiers, "Knowledge- -Based Intelligent Information and Engineering System" 2006, vol. 4251, s. 9-20, http://www.springerlink.com/index/07 m8415 wj 15v2677.pdf.
  9. Kyriakopoulou A., Kalamboukis T., Combining clustering with classification for spam detection in social bookmarking systems, 2008, http://ipl.cs.aueb.gr/publi- cations/Combining Clustering with Classification for Spam Detection in Social Bookmarking Systems.pdf.
  10. Małyszko J., Filipowska A., Abramowicz W., Kaczmarek T., Bukowska E., Perkowski B., Stolarski P. et al., Architektura systemu wykrywania zagrożeń w cyberprzestrzeni, "Roczniki" Kolegium Analiz Ekonomicznych SGH, z. 24, Oficyna Wydawnicza SGH, Warszawa 2012, s. 11-22.
  11. Markov A., Fast categorization of Web documents represented by graphs, "Advances in Web Mining and Web Usage Analysis" 2007, vol. 4811, s. 56-71, http://www. springerlink.com/index/u4886005 r4760437.pdf.
  12. Neumayer R., Clustering based ensemble classification for spam filtering, 2006, http://citeseerx.ist. psu.edu/viewdoc/download?doi= 10.1.1.140.9223&rep=repl &type=pdf.
  13. Ogura H., Amano H., Kondo M., Feature selection with a measure of deviations from Poisson in text categorization, "Expert Systems with Applications" 2009, vol. 36(3), s. 6826-6832, doi:10.1016/j.eswa.2008.08.006.
  14. Sebastiani E, Machine learning in automated text categorization, "ACM Computing Surveys" 2002, vol. 34(1), s. 1-47, doi:10.1145/505282.505283.
  15. Senator T.E., On the efficacy of data mining for security applications, w: Proceedings of the ACM SIGKDD Workshop on CyberSecurity and Intelligence Informatics - CSI-KDD '09, ACM Press, New York 2009, s. 75-83, http://dl.acm.org/citation. cfm?id= 1599272.1599286.
  16. Tong S., Koller D., Support vector machine active learning with applications to text classification, "The Journal of Machine Learning Research" 2002, vol. 2(1), s. 45-66, http://dl.acm.org/citation.cfm?id=944793.
  17. http://spamassassin.apache.org [dostęp 07.08.2012].
  18. The life of Spam Assassin Rule, http://taint.org/2005/08/06/024026a.html [dostęp 08.08.2012],
Cited by
Show
ISSN
1232-4671
Language
pol
Share on Facebook Share on Twitter Share on Google+ Share on Pinterest Share on LinkedIn Wyślij znajomemu