Virus detection using artificial immune system with genetic algorithm
Other Title(s)
الكشف عن الفيروسات باستخدام نظام المناعة الاصطناعي و الخوارزمية الجينية
Dissertant
Thesis advisor
al-Hamami, Ala H.
Abu Zitar, Raid
Comitee Members
Ubayd, Nadim
al-Ani, Muzhir Shaban
Kasasibah, Basil
University
Amman Arab University
Faculty
Collage of Computer Sciences and Informatics
Department
Department of Computer Science
University Country
Jordan
Degree
Ph.D.
Degree Date
2010
English Abstract
The protection against viruses is becoming increasingly difficult day by day, and they form risks on every one who uses computers, especially large companies and institutions.
The viruses' intelligence is accumulated with time, and their signatures are changing continuously, which has made the Anti-viruses mission more complicated.
Consequently, the issue of detecting viruses has been considered a hot and important topic.
This dissertation aims to develop an algorithm, which is based on the concepts of the Artificial Immune System to detect viruses.
Several studies have been concerned with the Artificial Immune System, which is inspired by the natural immune system of humans and animals.
This subject is relatively considered recent, and is not matured yet.
This system has been applied in different fields, most importantly viruses.
An algorithm has been suggested in this dissertation, which is based on the Artificial Immune System.
A clonal selection Algorithm has been developed to detect viruses, which has been written, programmed and called the Virus detection Clonal (VDC) algorithm.
The VDC algorithm consists of three basic steps: cloning, Hypermutation and re-selection stochastically.
Within the step of the reselection stochastically; there lay the virus’s detection process, where the viruses’ signatures are matched with the files.
The developed VDC algorithm is subjected to testing of two phases; training and matching.
Two main parameters are determined; one of them is setting the number of signatures per clone (Fat), while the other defines the Hypermutation probability (Pm).
Later on the researcher used Genetic Algorithm as a tool, to improve the developed algorithm in searching the values of the main parameters (Fat and Pm) to reproduce better results.
The dissertation results have shown that the detection rate of viruses, by using the developed algorithm, is 94.4%.
As for the detection rate of false positives, it has reached 0%.
These rates are confirmed by the Genetic Algorithm.
The Dissertation has concluded that the developed algorithm (VDC), which is created to detect viruses, is good, and can be used in this field.
The researcher has recommended that the developed algorithm can be utilized to be applied on other types of Malware that have signatures
Main Subjects
Topics
No. of Pages
166
Table of Contents
Table of contents.
Abstract.
Abstract in Arabic.
Chapter One : Introduction.
Chapter Two : Literature reviews and related works.
Chapter Three : Methodology.
Chapter Four : The VDC algorithm results and analysis.
Chapter Five : The optimization of the VDC algorithm using the GA.
Chapter Six : Conclusions and future work.
References.
American Psychological Association (APA)
Affanah, Suha M. A.. (2010). Virus detection using artificial immune system with genetic algorithm. (Doctoral dissertations Theses and Dissertations Master). Amman Arab University, Jordan
https://search.emarefa.net/detail/BIM-528614
Modern Language Association (MLA)
Affanah, Suha M. A.. Virus detection using artificial immune system with genetic algorithm. (Doctoral dissertations Theses and Dissertations Master). Amman Arab University. (2010).
https://search.emarefa.net/detail/BIM-528614
American Medical Association (AMA)
Affanah, Suha M. A.. (2010). Virus detection using artificial immune system with genetic algorithm. (Doctoral dissertations Theses and Dissertations Master). Amman Arab University, Jordan
https://search.emarefa.net/detail/BIM-528614
Language
English
Data Type
Arab Theses
Record ID
BIM-528614
