Abstract:
World has changed. Everybody is connected.
Almost each and everyone have a mobile phone. Millions of
SMSs are going around the world over mobile networks in
every second. But about 113 of them are spam. SMS spam has
become a crucial problem with the increase of mobile
penetration around the world. SMS spam filtering is a
relatively new task which inherits many issues and solutions
from email spam filtering. However it poses its own specific
challenges. Server based approaches and Mobile application
based approaches are accommodate content based and
content less mechanism to do the SMS spam filtering. Though
there are approaches, still there is a lack of a hybrid solution
which can do general filtering at server level while user
specific filtering can be done on mobile level. This paper
presents a hybrid solution for SMS spam filtering where both
feature phone users as well as smart phone users get benefited.
Feature phone users can experience the general filter while
smart phone users can configure and filter SMSs based on
their own preferences rather than sticking in to a general
filter. Server level solution consists of a neural network along
with a Bayesian filter and device level filter consists of a
Bayesian filter. We have evaluated the accuracy of neural
network using spam huge dataset along with some randomly
used personal SMSs.
