Intrusion detection model inspired by immune using K-means and naive Bayes as hybrid learning approach
Dissertant
Thesis advisor
Comitee Members
al-Tamimi, Abd al-Fattah
Naum, Riyad S.
University
Middle East University
Faculty
Faculty of Information Technology
Department
Computer Science Department
University Country
Jordan
Degree
Master
Degree Date
2011
English Abstract
Intrusions in computer networks can be compared to human diseases with the difference that human body has an effective mechanism to deal with them.
Human immune system can detect and defend against unseen intruders.
Also, it is distributed and adaptive.
Human immune system is the most powerful defense system which may be helpful to apply its mechanism and properties in computer security field.
This thesis presents a model for intrusion detection system that consists of four components depending on innate/adaptive human immune system approaches and self/non-self theory of human immune system.
The proposed model is divided into two subsystems; the first one is attack response system which is similar to innate human immune system and the second is learning system which is similar to adaptive immune system.
Learning system is the core of the model; it presents a hybrid approach of machine learning through hybridization between k-Means clustering algorithm and Naive Bayes as a classifier.
The model goal is keeping information systems environment safe against intrusions and attacks through applying human immune system mechanism and properties to intrusion detection system.
Experimental results indecate that the proposed model provide a higher detection rate in both of DoS attacks and U2R attacks, which give the power to the proposed hybrid model and increase the security of information systems, especially in the critical environments.
Main Subjects
Information Technology and Computer Science
No. of Pages
66
Table of Contents
Table of contents.
Abstract.
Abstract in Arabic.
Chapter One : Introduction.
Chapter Two : Intrusion detection and human immune (literature review)
Chapter Three : The proposed hybrid model architecture.
Chapter Four : Evaluation and experimental results.
Chapter Five : Conclusion and recommendations.
References.
American Psychological Association (APA)
Islim, Imad Faris Salim. (2011). Intrusion detection model inspired by immune using K-means and naive Bayes as hybrid learning approach. (Master's theses Theses and Dissertations Master). Middle East University, Jordan
https://search.emarefa.net/detail/BIM-699437
Modern Language Association (MLA)
Islim, Imad Faris Salim. Intrusion detection model inspired by immune using K-means and naive Bayes as hybrid learning approach. (Master's theses Theses and Dissertations Master). Middle East University. (2011).
https://search.emarefa.net/detail/BIM-699437
American Medical Association (AMA)
Islim, Imad Faris Salim. (2011). Intrusion detection model inspired by immune using K-means and naive Bayes as hybrid learning approach. (Master's theses Theses and Dissertations Master). Middle East University, Jordan
https://search.emarefa.net/detail/BIM-699437
Language
English
Data Type
Arab Theses
Record ID
BIM-699437
