Suspicious emails: unclaimed insurance bonds, diamond-encrusted safe deposit boxes, close friends marooned in a foreign country. They pop up in our inboxes, and standard procedure is to delete on sight. But what happens when you reply? Follow along as writer and comedian James Veitch narrates a hilarious, weeks-long exchange with a spammer who offered to cut him in on a hot deal.
My main research interest is on machine learning. So far I focussed mainly on kernel methods and learning with graphs. Recently, I am also developing interests in transductive and semi-supervised learning.
Sind Sie lästige Anrufe von Zeitschriftenverlagen, Telekommunikationsanbietern oder Lottovermittlern leid? Wenn selbst Gegenskript oder geheime Rufnummer nicht weiterhelfen, lassen Sie doch einfach Frank Ihre Anrufe entgegen nehmen. Frank geht ran, wenn Sie nicht wollen.
By clicking on the link below, you will be given a temporary e-mail address. Any e-mails sent to that address will show up automatically on the web page. You can read them, click on links, and even reply to them. The e-mail address will expire after 10 minutes.
Web spam pages use various techniques to achieve
higher-than-deserved rankings in a search engine’s
results. While human experts can identify
spam, it is too expensive to manually evaluate a
large number of pages. Instead, we propose techniques
to semi-automatically separate reputable,
good pages from spam. We first select a small set
of seed pages to be evaluated by an expert. Once
we manually identify the reputable seed pages, we
use the link structure of the web to discover other
pages that are likely to be good. In this paper
we discuss possible ways to implement the seed
selection and the discovery of good pages. We
present results of experiments run on the World
Wide Web indexed by AltaVista and evaluate the
performance of our techniques. Our results show
that we can effectively filter out spam from a significant
fraction of the web, based on a good seed
set of less than 200 sites.
C. Kater, and R. Jäschke. Proceedings of the 1st International Workshop on Online Safety, Trust and Fraud Prevention, page 2:1--2:6. New York, NY, USA, ACM, (June 2016)
B. Krause, C. Schmitz, A. Hotho, and G. Stumme. Proceedings of the 4th International Workshop on Adversarial Information Retrieval on the Web, page 61--68. New York, NY, USA, ACM, (2008)
Y. Chung, M. Toyoda, and M. Kitsuregawa. Proceedings of the 5th International Workshop on Adversarial Information Retrieval on the Web, page 9--16. New York, NY, USA, ACM, (2009)
B. Bullock, H. Lerch, A. Roßnagel, A. Hotho, and G. Stumme. Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies, page 15:1--15:8. New York, NY, USA, ACM, (2011)
M. Atzmueller, F. Lemmerich, B. Krause, and A. Hotho. Proc. LeGo-09: From Local Patterns to Global Models, Workshop at the 2009 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, (2009)accepted.
B. Krause, C. Schmitz, A. Hotho, and G. Stumme. AIRWeb '08: Proceedings of the 4th International Workshop on Adversarial Information Retrieval on the Web, page 61--68. New York, NY, USA, ACM, (April 2008)
B. Krause, C. Schmitz, A. Hotho, and G. Stumme. AIRWeb '08: Proceedings of the 4th international workshop on Adversarial information retrieval on the web, page 61--68. New York, NY, USA, ACM, (2008)
J. Hidalgo, G. Bringas, E. Sánz, and F. García. DocEng '06: Proceedings of the 2006 ACM symposium on Document engineering, page 107--114. New York, NY, USA, ACM Press, (2006)