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Detection of Denial of Service Attacks against Domain Name System Using Neural Networks

Along with the explosive growth of the Internet, the demand for efficient and secure Internet Infrastructure has been increasing. For the entire chain of Internet connectivity the Domain Name System (DNS) provides name to address mapping services. Hackers exploit this fact to damage different par...

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Main Author: Rastegari, Samaneh
Format: Thesis
Language:English
English
Published: 2009
Online Access:http://psasir.upm.edu.my/id/eprint/7302/1/FK_2009_23a.pdf
http://psasir.upm.edu.my/id/eprint/7302/
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spelling my.upm.eprints.73022013-05-27T07:34:32Z http://psasir.upm.edu.my/id/eprint/7302/ Detection of Denial of Service Attacks against Domain Name System Using Neural Networks Rastegari, Samaneh Along with the explosive growth of the Internet, the demand for efficient and secure Internet Infrastructure has been increasing. For the entire chain of Internet connectivity the Domain Name System (DNS) provides name to address mapping services. Hackers exploit this fact to damage different parts of Internet. In order to prevent this system from different types of attacks, we need to prepare a classification of possible security threats against DNS. This dissertation focuses on Denial of Service (DoS) attacks as the major security issue during last years, and gives an overview of techniques used to discover and analyze them. The process of detection and classification of DoS against DNS has been presented in two phases in our model. The proposed system architecture consists of a statistical pre-processor and a machine learning engine. The first step in our work was to generate the DNS traffic in normal and attack situations for using as the input of our intrusion detection system (IDS). With the prior knowledge of DoS attacks against DNS, we used a network simulator to model DNS traffic with high variability. Therefore, the difficulty of creating different scenarios of attacks in a real environment has been decreased. The pre-processor, processes the collected data statistically and derives the final variable values. These parameters are the inputs of the detector engine. In the current research for our machine learning engine, we aimed to find the optimum machine learning algorithm to be used as an IDS. The performance of our system was measured in terms of detection rate, accuracy, and false alarm rate. The results indicated that the three layered back propagation neural network with a 3-7-3 structure provides a detection rate of 99.55% for direct DoS attacks and 97.82% for amplification DoS attacks. It can give us 99% accuracy and an acceptable false alarm rate of 0.28% comparing to other types of classifiers. 2009-08 Thesis NonPeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/7302/1/FK_2009_23a.pdf Rastegari, Samaneh (2009) Detection of Denial of Service Attacks against Domain Name System Using Neural Networks. Masters thesis, Universiti Putra Malaysia. English
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
English
description Along with the explosive growth of the Internet, the demand for efficient and secure Internet Infrastructure has been increasing. For the entire chain of Internet connectivity the Domain Name System (DNS) provides name to address mapping services. Hackers exploit this fact to damage different parts of Internet. In order to prevent this system from different types of attacks, we need to prepare a classification of possible security threats against DNS. This dissertation focuses on Denial of Service (DoS) attacks as the major security issue during last years, and gives an overview of techniques used to discover and analyze them. The process of detection and classification of DoS against DNS has been presented in two phases in our model. The proposed system architecture consists of a statistical pre-processor and a machine learning engine. The first step in our work was to generate the DNS traffic in normal and attack situations for using as the input of our intrusion detection system (IDS). With the prior knowledge of DoS attacks against DNS, we used a network simulator to model DNS traffic with high variability. Therefore, the difficulty of creating different scenarios of attacks in a real environment has been decreased. The pre-processor, processes the collected data statistically and derives the final variable values. These parameters are the inputs of the detector engine. In the current research for our machine learning engine, we aimed to find the optimum machine learning algorithm to be used as an IDS. The performance of our system was measured in terms of detection rate, accuracy, and false alarm rate. The results indicated that the three layered back propagation neural network with a 3-7-3 structure provides a detection rate of 99.55% for direct DoS attacks and 97.82% for amplification DoS attacks. It can give us 99% accuracy and an acceptable false alarm rate of 0.28% comparing to other types of classifiers.
format Thesis
author Rastegari, Samaneh
spellingShingle Rastegari, Samaneh
Detection of Denial of Service Attacks against Domain Name System Using Neural Networks
author_facet Rastegari, Samaneh
author_sort Rastegari, Samaneh
title Detection of Denial of Service Attacks against Domain Name System Using Neural Networks
title_short Detection of Denial of Service Attacks against Domain Name System Using Neural Networks
title_full Detection of Denial of Service Attacks against Domain Name System Using Neural Networks
title_fullStr Detection of Denial of Service Attacks against Domain Name System Using Neural Networks
title_full_unstemmed Detection of Denial of Service Attacks against Domain Name System Using Neural Networks
title_sort detection of denial of service attacks against domain name system using neural networks
publishDate 2009
url http://psasir.upm.edu.my/id/eprint/7302/1/FK_2009_23a.pdf
http://psasir.upm.edu.my/id/eprint/7302/
_version_ 1643823682346287104
score 13.211869

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