Distributed Denial of Service Attacks Detection Using Support Vector Machine

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Oct 16, 2013 (4 years and 23 days ago)

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Distributed Denial of Service Attacks Detection Using Support Vector
Machine







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Author(s):

Ahmad I

(Ahmad, Iftikhar)
1
,
2
,

Abdullah AB

(Abdullah, Azween B.)
1
,

Alghamdi AS

(Alghamdi,
Abdullah S.)
2
,

Hussain M

(Hussain, Muhammad)
3

Source:

INFORMATION
-
AN INTERNATIONAL INTERDISCIPLINARY
JOURNAL

Volume:

14

Issue:

1

Pages:

127
-
134

Published:

JAN 2011


Times Cited:

0

References:

18

Citation Map


Abstract:

Attacks on the networks are most important issues. Therefore, the prevention of such attacks
is imperative. The hindrance of such attacks is exclusively depe
ndent on their detection. The detection
is a prime part of any security tool such as Intrusion Detection System (IDS), Intrusion Prevention
System (IPS), Adaptive Security Alliance (ASA), check points and firewalls. A variety of intrusion
detection approac
hes be present to resolve this severe issue but the main problem is performance.
Therefore, in this paper, a model is proposed to overcome performance issues. In this model, support
vector machine (SVM) and backpropagation neural network are applied on dis
tributed denial of service
(DDOS) attacks. The system uses sampled data form cooperative association for internet data analysis
(CAIDA) dataset, an attack database that is a standard for evaluating the security detection
mechanisms. The results and compara
tive studies indicate that the proposed mechanism demonstrate
more accuracy in case of false positive, false negative and detection rate.

Document Type:

Article

Language:

English

Author Keywords:

Neural Network Intrusion Detection (NNID); Support Vector

Machine (SVM);
Cooperative Association for Internet Data Analysis (CAIDA); Distributed Denial of Service (DDOS);
False Positive (FP); False Negative (FN); True Positive (TP); True Negative (TN)

KeyWords Plus:

INTRUSION
-
DETECTION; NEURAL
-
NETWORKS

Reprint

Address:

Ahmad, I (reprint author), Univ Teknol PETRONAS, Dept Comp & Informat Sci,
Tronoh 31750, Perak Malaysia

Addresses:


1. Univ Teknol PETRONAS, Dept Comp & Informat Sci, Tronoh 31750, Perak Malaysia


2. King Saud Univ, Coll Comp & informat Sci, Dep
t Software Engn, Riyadh 11543, Saudi Arabia


3. King Saud Univ, Coll Comp & informat Sci, Dept Comp Sci, Riyadh 11543, Saudi Arabia

E
-
mail Addresses:

wattoohu@gmail.com
,

m
hussain@ksu.edu.sa

Funding Acknowledgement:

Funding Agency

Grant Number

Department of Software Engineering, College of Computer and Information Sciences,
King Saud University, Saudi Arabia





[Show funding text]


Publisher:

INT INFORMATION INST, FAC ENG, HOSEI UNIV, KOGANEI, TOKYO, 184
-
8584, JAPAN

Subject Category:

Engineering, Multidisciplinary

IDS Number:

739PZ

ISSN:

1343
-
4500