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Reseach Article

ANN to Detect Network under Black Hole Attack

Published on September 2015 by Alfy Augustine, Manju James
International Conference on Emerging Trends in Technology and Applied Sciences
Foundation of Computer Science USA
ICETTAS2015 - Number 1
September 2015
Authors: Alfy Augustine, Manju James
619fb4ac-7176-475f-9e9f-a8b257b64abe

Alfy Augustine, Manju James . ANN to Detect Network under Black Hole Attack. International Conference on Emerging Trends in Technology and Applied Sciences. ICETTAS2015, 1 (September 2015), 15-18.

@article{
author = { Alfy Augustine, Manju James },
title = { ANN to Detect Network under Black Hole Attack },
journal = { International Conference on Emerging Trends in Technology and Applied Sciences },
issue_date = { September 2015 },
volume = { ICETTAS2015 },
number = { 1 },
month = { September },
year = { 2015 },
issn = 0975-8887,
pages = { 15-18 },
numpages = 4,
url = { /proceedings/icettas2015/number1/22373-2567/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Emerging Trends in Technology and Applied Sciences
%A Alfy Augustine
%A Manju James
%T ANN to Detect Network under Black Hole Attack
%J International Conference on Emerging Trends in Technology and Applied Sciences
%@ 0975-8887
%V ICETTAS2015
%N 1
%P 15-18
%D 2015
%I International Journal of Computer Applications
Abstract

Security related issues are of serious concern in MANET. Lack of central administration and shared wireless medium makes MANET more vulnerable to security threats. An intruder passes an intermediate node into the MANET and introduces several kinds of attacks on the data transfer occurring between nodes. In this paper we consider Black hole attack in mobile ad hoc network, where all data packets are absorbed by the malicious nodes. A mechanism based on Artificial Neural Network (ANN) to detect the network under Black hole attack employing AODV routing protocol has been designed.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Ad Hoc Network Black Hole Attack Artificial Neural Network