Research Projects
Spam
Spam related cyber crimes are a serious problem.
Current spam research focuses mainly on detecting and filtering spam.
We believe that the identification and disruption of the supporting
infrastructure used by spammers is a more effective way of
stopping spam than filtering.
The termination of spam hosts will greatly reduce the profit a
spammer can generate and thwart his ability to send more spam.
This research proposes to cluster spam messages according to their origin.
This will show which actors are the most prolific producers of spam,
which can then be the focus of law enforcement investigators' efforts.
Spam Data Mine
Grammar inference
Grammar Inference (GI) is the process of learning a grammar from examples,
either positive (i.e., the grammar does generate the string)
or both positive and negative.
Two goals of this research are
1. Recovery of domain-specific language (DSL) specification
from example DSL programs.
2. Application of GI to subjects of spam eamil messages within a cluster,
to learn the template used to create them; this will be a more powerful
alternative to the string matching which we now employ.
This work is supported by the National Science Foundation under Grant No. 0811630.
Grammar Inference web page
Grain growth in metals
Simulation of grain growth. Apply Data Mining to the output file
of topological changes, to investigate the patterns that emerge.