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Enhancing associative classification technique for spam detection
Dissertant
Jarrah, Muhammad Ahmad Muhammad
Thesis advisor
University
Philadelphia University
Faculty
Faculty of Information Technology
Department
Department of Computer Science
University Country
Jordan
Degree
Master
Degree Date
2017
English Abstract
Data mining techniques can extract meaning from noisy data, find out patterns in random data, and use this information to best understand trends, patterns, connection and relations between the data.
Data mining has two classes (supervised and unsupervised) depending on the human management or predefining the goal.
One of the hybrid techniques which combine supervised and unsupervised technique to achieve a full automated process to find hidden patterns and classify the data depending on this patterns and relations is the Associative classification AC.This technique is widely used in the world to solve many problems.
In this study we propose a new enhanced associative classification approach work to increase the accuracy of it by decreasing the number of inaccurate rules that may obtained by the association rule mining by adding a second rule checking step to solve this problem and proved that by the experimental results.
The proposed approach applied in the cyber security domain exactly to solve the SPAM emails problem as a SPAM detection hybrid algorithm (join link based (header) and content base).
The implementation of this approach shows that the proposed approach improves the performance of the traditional approach in different aspects .
For example the precision measure increased from 0.543 to 0.862 and the accuracy measure also increased from 0.721 to 0.890compared with the traditional approach.
Main Subjects
Information Technology and Computer Science
No. of Pages
51
Table of Contents
Table of contents.
Abstract.
Abstract in Arabic.
Chapter One : Introduction.
Chapter Two : Literature review.
Chapter Three : Proposed mode.
Chapter Four : Data and experimental results.
Chapter Five : Conclusions and future works.
References.
American Psychological Association (APA)
Jarrah, Muhammad Ahmad Muhammad. (2017). Enhancing associative classification technique for spam detection. (Master's theses Theses and Dissertations Master). Philadelphia University, Jordan
https://search.emarefa.net/detail/BIM-955865
Modern Language Association (MLA)
Jarrah, Muhammad Ahmad Muhammad. Enhancing associative classification technique for spam detection. (Master's theses Theses and Dissertations Master). Philadelphia University. (2017).
https://search.emarefa.net/detail/BIM-955865
American Medical Association (AMA)
Jarrah, Muhammad Ahmad Muhammad. (2017). Enhancing associative classification technique for spam detection. (Master's theses Theses and Dissertations Master). Philadelphia University, Jordan
https://search.emarefa.net/detail/BIM-955865
Language
English
Data Type
Arab Theses
Record ID
BIM-955865