Genetic based machine learning system for mp3 steganalysis
Dissertant
Thesis advisor
Comitee Members
Kanan, Ghassan Jaddu
Uways, Suhayl
University
Arab Academy for Financial and Banking Sciences
Faculty
The Faculty of Information Systems and Technology
Department
Computer information systems
University Country
Jordan
Degree
Ph.D.
Degree Date
2010
English Abstract
Moving Picture Expert Group-3 MP3 steganography is the art of hiding data into digital MP3 files.
This science represents a threat to the safeguarding of sensitive information and the gathering of intelligence.
Steganalysis is the art of detecting this hidden information.
It is an inherently difficult problem and requires a thorough investigation.
A strong framework for analysis is required for steganalyst and the steganographer to improve its task in the hiding process inside of steganographer and detection process inside of steganalyst.
In this dissertation, we lie down of foundation of a passive Genetic Base Machine Learning GBML framework for analysis of steganography and steganalysis and use this analysis to create practical solutions to the problems of detecting and evading detection.
GBML previously employed in disciplines such as networking, clustering, and classification to provide a natural framework for studying different type of problems.
With this framework, we make statements on the detectability of modern MP3 steganography schemes, develop tools for steganalysis in a practical scenario, and design and analyze a mean of escaping optimal detection.
We develop our detection framework and apply it to detect the steganalysis of MP3StegC, MP3StegZ, MP3StegInfo and Stegangraphy1.8 hiding schemes.
Experiments over a diverse database of MP3 files show our steganalysis to be effective and competitive with the state-of-the-art.
Though our approach has gained satisfying results in the study of steganography, we acknowledge there are problems yet to be solved in order to improve the detection rate.
Results show the Degree Of Confidence DOC of the system on the average is 0.84 for clear files and 0.88 for stego files, while Measure Of Usefulness MOU on the average for MP3StegC, MP3StegInfo, MP3StegZ, and Steganography 1.8 are 0.64, 0.74, 0.73, and 0.90 respectively.
Main Subjects
Information Technology and Computer Science
Topics
No. of Pages
121
Table of Contents
Table of contents.
Abstract.
Chapter One : introduction.
Chapter Two : literature review.
Chapter Three : research background.
Chapter Four : steganography and steganalysis.
Chapter Five : MP3 anatomy.
Chapter Six : framework implementation.
Chapter Seven : results and conclusions.
References.
American Psychological Association (APA)
al-Buwainah, Muhammad Jazi. (2010). Genetic based machine learning system for mp3 steganalysis. (Doctoral dissertations Theses and Dissertations Master). Arab Academy for Financial and Banking Sciences, Jordan
https://search.emarefa.net/detail/BIM-307532
Modern Language Association (MLA)
al-Buwainah, Muhammad Jazi. Genetic based machine learning system for mp3 steganalysis. (Doctoral dissertations Theses and Dissertations Master). Arab Academy for Financial and Banking Sciences. (2010).
https://search.emarefa.net/detail/BIM-307532
American Medical Association (AMA)
al-Buwainah, Muhammad Jazi. (2010). Genetic based machine learning system for mp3 steganalysis. (Doctoral dissertations Theses and Dissertations Master). Arab Academy for Financial and Banking Sciences, Jordan
https://search.emarefa.net/detail/BIM-307532
Language
English
Data Type
Arab Theses
Record ID
BIM-307532
