Detection and classification of DDoS attack using artificial neural network
Dissertant
al-Musayidin, Muhammad Abd Allah
Thesis advisor
al-Kasasibah, Muhammad Sharari Zamil
Comitee Members
al-Abbadi, Muhammad Ali Husayn
al-Hasanat, Ahmad Bashir
al-Sarayirah, Jafar Muhammad
University
Mutah University
Faculty
Information Technology College
University Country
Jordan
Degree
Master
Degree Date
2015
English Abstract
Distributed denial of service (DDoS) attacks are considered an ongoing challenge for users and organizations.
The security engineer works to maintain a service at all times by dealing with intruder attacks.
Intrusion-detection systems (IDS) are one of the solutions used to detect and classify any abnormal behavior.
An IDS system must be constantly updated with all the latest techniques to deal with intruder attacks in order to preserve service availability.
In this thesis, we study the effects of DDoS attacks in both the network layer and application layer, including most modern DDoS attacks such as (SIDDOS and HTTP Flood) attacks.
We have also created a system to collect a dataset from a controlled environment using a network simulator.
The dataset was generated through the following stages: data collecting, data preprocessing and classification.
Unlike other datasets, the proposed dataset includes 2,160,668 records with 28 attributes and with no duplicate records.
The proposed dataset includes four types of attacks, organized as follows: (Smurf, UDP-Flood, HTTP-Flood, and SIDDOS).
Multilayer Perceptron (MLP), Naïve Bayes and Random Forest algorithms were used for training and testing on the proposed dataset to evaluate the dataset models.
The MLP classifier achieved the highest accuracy rate (98.63%) for detecting and classifying DDoS attacks with the longest time for building the training model; the Random Forest classifier achieved 98.01% for detecting and classifying DDoS attacks; the Naïve Bayes achieved 96.91% for detecting and classifying DDoS attacks, and therefore the Naïve Bayes classifier achieved the fastest time for building the training model.
Main Subjects
Information Technology and Computer Science
No. of Pages
78
Table of Contents
Table of contents.
Abstract.
Abstract in Arabic.
Chapter One : Introduction.
Chapter Two : Literature review.
Chapter Three : Design and methodology.
Chapter Four : Findings, discussion and recommendations.
References.
American Psychological Association (APA)
al-Musayidin, Muhammad Abd Allah. (2015). Detection and classification of DDoS attack using artificial neural network. (Master's theses Theses and Dissertations Master). Mutah University, Jordan
https://search.emarefa.net/detail/BIM-729778
Modern Language Association (MLA)
al-Musayidin, Muhammad Abd Allah. Detection and classification of DDoS attack using artificial neural network. (Master's theses Theses and Dissertations Master). Mutah University. (2015).
https://search.emarefa.net/detail/BIM-729778
American Medical Association (AMA)
al-Musayidin, Muhammad Abd Allah. (2015). Detection and classification of DDoS attack using artificial neural network. (Master's theses Theses and Dissertations Master). Mutah University, Jordan
https://search.emarefa.net/detail/BIM-729778
Language
English
Data Type
Arab Theses
Record ID
BIM-729778
