Effect of Genetic Algorithm on Artificial Neural Network for Intrusion Detection System
A. Dastanpour1 , S. Ibrahim2 , R. Mashinchi3
Section:Research Paper, Product Type: Journal Paper
Volume-4 ,
Issue-10 , Page no. 10-18, Oct-2016
Online published on Oct 28, 2016
Copyright © A. Dastanpour, S. Ibrahim, R. Mashinchi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Style Citation: A. Dastanpour, S. Ibrahim, R. Mashinchi, “Effect of Genetic Algorithm on Artificial Neural Network for Intrusion Detection System,” International Journal of Computer Sciences and Engineering, Vol.4, Issue.10, pp.10-18, 2016.
MLA Style Citation: A. Dastanpour, S. Ibrahim, R. Mashinchi "Effect of Genetic Algorithm on Artificial Neural Network for Intrusion Detection System." International Journal of Computer Sciences and Engineering 4.10 (2016): 10-18.
APA Style Citation: A. Dastanpour, S. Ibrahim, R. Mashinchi, (2016). Effect of Genetic Algorithm on Artificial Neural Network for Intrusion Detection System. International Journal of Computer Sciences and Engineering, 4(10), 10-18.
BibTex Style Citation:
@article{Dastanpour_2016,
author = {A. Dastanpour, S. Ibrahim, R. Mashinchi},
title = {Effect of Genetic Algorithm on Artificial Neural Network for Intrusion Detection System},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {10 2016},
volume = {4},
Issue = {10},
month = {10},
year = {2016},
issn = {2347-2693},
pages = {10-18},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1072},
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1072
TI - Effect of Genetic Algorithm on Artificial Neural Network for Intrusion Detection System
T2 - International Journal of Computer Sciences and Engineering
AU - A. Dastanpour, S. Ibrahim, R. Mashinchi
PY - 2016
DA - 2016/10/28
PB - IJCSE, Indore, INDIA
SP - 10-18
IS - 10
VL - 4
SN - 2347-2693
ER -
VIEWS | XML | |
1856 | 1675 downloads | 1505 downloads |
Abstract
By increasing the advantages of network based systems and dependency of daily life with them, the efficient operation of network based systems is an essential issue. Since the number of attacks has significantly increased, intrusion detection systems of anomaly network behavior have increasingly attracted attention among research community. Intrusion detection systems have some capabilities such as adaptation, fault tolerance, high computational speed, and error resilience in the face of noisy information. So, construction of efficient intrusion detection model is highly required for increasing the detection rate as well as decreasing the false detection. . This paper investigates applying the following methods to detect the attacks intrusion detection system and understand the effective of GA on the ANN result: artificial Neural Network (ANN) for recognition and used Genetic Algorithm (GA) for optimization of ANN result. We use KDD CPU 99 dataset to obtain the results; witch shows the ANN result before the efficiency of GA and compare the result of ANN with GA optimization.
Key-Words / Index Term
Artificial Neural Network (ANN); Intrusion detection; Genetic algorithm (GA); Machine learning; Network Security
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