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Machine learning for user authentication using keystroke dynamics
Other Title(s)
استخدام ديناميات المفاتيح في تعليم الآلة من أجل مصادقة المستخدم
Dissertant
Thesis advisor
al-Kasasibah, Muhammad Sharari Zamil
Comitee Members
al-Abbadi, Muhammad Ali Husayn
al-Hasanat, Ahmad Bashir
Faris, Husam
University
Mutah University
Faculty
Information Technology College
University Country
Jordan
Degree
Master
Degree Date
2015
English Abstract
This thesis presents a methodology for improving the security of an authentication process: Keystroke Dynamics (KSD).
KSD is considered a behavioral biometric, operating as a second level of security along with the log-in process after inserting user name and password.
KSD is mainly about observing the way in which the user types.
In this thesis, firstly, we propose 4 time features in addition to the main three features, these features represent the user’s behavior, which will be used in the authentication phase.
Secondly, because of the lack of datasets in this field and because there is no standard dataset, we built a new dataset consisting of 504 records: 9 attempts for 56 users.
Thirdly, we proposed employing KSD in CAPCHA Code.
We supposed three cases; first case is when a program hacks the CAPTCHA code and sends the code directly, where accuracy is certain to produce a result of 100%, and therefore there is no need to build a dataset for this.
The second case is when the hacker is smarter, hacking the source code and knowing that there are features that must be sent with the code, therefore the hacker generates features with the code randomly.
In this case, the best accuracy results achieved were 98.13% for Random Forest and J48 classifier.
The third case is when the hacker is even smarter and hacks the source code, knowing that there is a relationship between the features and making the right calculations for the features, so that the main features are set randomly and others are calculated.
In this case, the best accuracy results achieved were 93.125% by using Multi-Layer Perceptron (MLP).
After that we run the validation process, and generated new dataset for that and obtained best accuracy94.76% using Random Forest classifier.
Finally, for the authentication users, we selected 20 users randomly.
Our results were convergent; the average of the accuracy results for MLP was 94.90%, 91.53% when using Random Forest and 89.68% when using J48.
Main Subjects
Information Technology and Computer Science
No. of Pages
63
Table of Contents
Table of contents.
Abstract.
Abstract in Arabic.
Chapter One : Introduction.
Chapter Two : Literature review
Chapter Three : Design and methodology
Chapter Four : Experiments and results.
Conclusions and future work.
References.
American Psychological Association (APA)
al-Tarawinah, Ahmad Ayman. (2015). Machine learning for user authentication using keystroke dynamics. (Master's theses Theses and Dissertations Master). Mutah University, Jordan
https://search.emarefa.net/detail/BIM-729783
Modern Language Association (MLA)
al-Tarawinah, Ahmad Ayman. Machine learning for user authentication using keystroke dynamics. (Master's theses Theses and Dissertations Master). Mutah University. (2015).
https://search.emarefa.net/detail/BIM-729783
American Medical Association (AMA)
al-Tarawinah, Ahmad Ayman. (2015). Machine learning for user authentication using keystroke dynamics. (Master's theses Theses and Dissertations Master). Mutah University, Jordan
https://search.emarefa.net/detail/BIM-729783
Language
English
Data Type
Arab Theses
Record ID
BIM-729783