Abstract:
Today text messages have a signi cant impact on our lives, but at the same time we
face many critical problems due to SMS spamming. Therefore to detect spam messages
and distinguish it via accurate ltering is a challenging task for researchers.
In current research work content based spam SMS ltering technique is proposed
depending on machine learning approach to distinguish spam messages from mobile
data while considering the low processing power and limited memory of mobile
phones. From each text message; ve attributes are extracted and based on
these attributes/features, an unknown/unlabelled SMS message can be classi ed
as spam or ham by a trained learning algorithm. These attributes are the length
of the text message and presence of repeatedly occurring spam words, count of
spam words, combination of spam words and SMS class. It is shown that Decision
Tree classi er performance is better than other machine learning algorithms
investigated. The other learning algorithms explored in this work are Nave Bayes
and neural network architecture-Multilayer Perceptron Algorithm.
