Abstract:
over the past few years, mobile devices and
their services have become widely used around the world.
Almost everyone uses the Text Messaging Service (SMS) for
communication purposes because it is easy to use and
inexpensive. When a person tries to deceive another for the
sake of profit (material or money), it is known as Fraud [1].
Through SMS fraud, fraudsters often adopt various strategies
to make their messages look credible and legitimate. Various
popular organizations use SMS services to advertise their
products and send messages to individuals about their services.
As a result, one receives many junk messages. Spam message is
a message sent to any user who does not want to have it on
their phone [2]. Spam or fraudulent messages can be
threatening and can sometimes cause financial and confidential
data loss. In Pakistan, messages are sent in English and Urdu
(Pakistani national language) but most messages are sent using
Roman Urdu (Urdu written using Latin / English characters).
This research compares the strategies and algorithms used in
the literature to detect spam / fraudulent messages written in
English or in any local language such as Roman Urdu. The
study also suggests a new way to detect fraudulent messages
written directly in Roman Urdu. In the fraud detection
process, three different monitoring machine learning classifiers
are used in this study namely Support Vector Machine (SVM)
[11], Naïve Bayes (NB) [12] and Decision Tree (J48) [13]. After
using the model, we found that SVM performed better than the
other two classifiers with 99.42% accuracy
