'Identifying Malicious URLs: An Application of Large-Scale online Learning' (ICML-09)
Justin Ma, Lawrence K. Saul, Stefan Savage, Geoffrey M. Voelker

Please visit [http://sysnet.ucsd.edu/projects/url/] for more information.

Data Set Information:

Uncompressing the archive url_svmlight.tar.gz will yield a directory url_svmlight/ containing the following files:
   * FeatureTypes --- A text file list of feature indices that correspond to real-valued features.
   * DayX.svm (where X is an integer from 0 to 120) --- The data for day X in SVM-light format. A label of +1 corresponds to a malicious URL and -1 corresponds to a benign URL.

Attribute Information:

Attributes are anonymized, but correspond to lexical and host-based features gathered for each URL.

Relevant Papers:

N/A

Citation Request:

If you use this data set in published work, please cite the ICML-09 paper in which it was first introduced and described:

Justin Ma, Lawrence K. Saul, Stefan Savage, and Geoffrey M. Voelker,
Identifying Suspicious URLs: An Application of Large-Scale online Learning
Proceedings of the International Conference on Machine Learning (ICML), pages 681-688, Montreal, Quebec, June 2009.