Arabic SMS spam detection based on semantic classification
Other Title(s)
كشف الرسائل العربية القصيرة المزعجة اعتمادا على التصنيف الدلالي
Dissertant
Abu Awdah, Ayman Muhammad Najib Ismail
Thesis advisor
University
Islamic University
Faculty
Faculty of Information Technology
Department
Information Technology
University Country
Palestine (Gaza Strip)
Degree
Master
Degree Date
2017
English Abstract
Short Messaging Service (SMS) Spam is unwanted messages sent over the web or mobile system to mobile phone devices.
SMS is attractive for spammers due to its cheap services, easily deciding the destination country and its higher response rates than email.
Existing solutions to this issue are no longer adequate as they are either costly in terms of resources, inefficient, most of the existing detection techniques for SMS spam have been adapted from other contexts such as email spam detection methods.
Spammers are constantly developing more sophisticated tactics causing previous methods for spam detection as ineffective.
Additionally, when it comes to Arabic SMS messages, most SMS spam filtering system based on English language.
This research presents an Arabic SMS spam detection and classification approach using ontology with semantic rules.
An Arabic SMS spam ontology with a support of Arabic WordNet is built by defining spam classes and hierarchy and adding a collection of various spam messages as instances creating a knowledge base reflecting the domain.
To enable the detection and classification of messages based on the knowledge base, a set of SWRL rules were written.
These rules are used by the reasoner to filter out messages as spam or legitimate.
Based on the enriched knowledge base, an SMS spam detection system is built.
It consists of several modules such as query module, reasoning module, synonym module, SMS module and finally classifier module.
The approach is evaluated based on its ability to classify and detect SMS messages as spam or legitimate.
A number of performance measures are used for this purpose.
The evaluation resulted in an accuracy of 96.5% and in a f-measure of 90.5% which are better than those achieved using a traditional classifier such as Naïve Bayes.
Main Subjects
Information Technology and Computer Science
No. of Pages
92
Table of Contents
Table of contents.
Abstract.
Abstract in Arabic.
Chapter One : Introduction.
Chapter Two : Theoretical and technical foundation.
Chapter Three : Related works.
Chapter Four : Arabic SMS spam ontology.
Chapter Five : Arabic SMS spam detection.
Chapter Six : Results and discussion.
Chapter Seven : Conclusion and future work.
References.
American Psychological Association (APA)
Abu Awdah, Ayman Muhammad Najib Ismail. (2017). Arabic SMS spam detection based on semantic classification. (Master's theses Theses and Dissertations Master). Islamic University, Palestine (Gaza Strip)
https://search.emarefa.net/detail/BIM-905275
Modern Language Association (MLA)
Abu Awdah, Ayman Muhammad Najib Ismail. Arabic SMS spam detection based on semantic classification. (Master's theses Theses and Dissertations Master). Islamic University. (2017).
https://search.emarefa.net/detail/BIM-905275
American Medical Association (AMA)
Abu Awdah, Ayman Muhammad Najib Ismail. (2017). Arabic SMS spam detection based on semantic classification. (Master's theses Theses and Dissertations Master). Islamic University, Palestine (Gaza Strip)
https://search.emarefa.net/detail/BIM-905275
Language
English
Data Type
Arab Theses
Record ID
BIM-905275