A sequence number prediction based bait detection scheme to mitigate sequence number attacks in MANETs

The characteristics of MANET such as decentralized architecture, dynamic topologies make MANETs susceptible to various security attacks. Sequence number attacks are such type of security threats which tend to degrade the network functioning and performance by sending fabricated route reply packets (...

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Main Authors: Jhaveri, Rutvij H., Desai, Aneri, Patel, Ankit, Zhong, Yubin
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2019
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Online Access:https://hdl.handle.net/10356/105333
http://hdl.handle.net/10220/48081
https://doi.org/10.1155/2018/3210207
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1053332019-12-06T21:49:18Z A sequence number prediction based bait detection scheme to mitigate sequence number attacks in MANETs Jhaveri, Rutvij H. Desai, Aneri Patel, Ankit Zhong, Yubin School of Electrical and Electronic Engineering Delta-NTU Corporate Laboratory Mobile Security Mobile Ad Hoc Networks DRNTU::Engineering::Computer science and engineering The characteristics of MANET such as decentralized architecture, dynamic topologies make MANETs susceptible to various security attacks. Sequence number attacks are such type of security threats which tend to degrade the network functioning and performance by sending fabricated route reply packets (RREP) with the objective of getting involved in the route and drop some or all of the data packets during the data transmission phase.The sequence number adversary attempts to send a fabricated high destination number in the RREP packet which attracts the sender to establish a path through the adversary node. This paper proposes a proactive secure routing mechanism which is an improvement over the authors previously proposed scheme. It makes use of linear regression mechanism to predict the maximum destination sequence number that the neighboring node can insert in the RREP packet. As an additional security checkpoint, it uses a bait detection mechanism to establish confidence in marking a suspicious node as a malicious node.The proposed approach works in collaboration with the ad hoc on-demand distance vector routing (AODV) protocol. The simulation results depict that the approach improves the network performance in the presence of adversaries as compared to previously proposed scheme. Published version 2019-04-26T06:14:14Z 2019-12-06T21:49:18Z 2019-04-26T06:14:14Z 2019-12-06T21:49:18Z 2018 2018 Journal Article Jhaveri, R. H., Desai, A., Patel, A., & Zhong, Y. (2018). A sequence number prediction based bait detection scheme to mitigate sequence number attacks in MANETs. Security and Communication Networks, 20181-13. doi:10.1155/2018/3210207 1939-0114 https://hdl.handle.net/10356/105333 http://hdl.handle.net/10220/48081 https://doi.org/10.1155/2018/3210207 209920 en Security and Communication Networks © 2018 Rutvij H. Jhaveri et al.This is an open access article distributed under theCreativeCommonsAttribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 14 p. application/pdf
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Mobile Security
Mobile Ad Hoc Networks
DRNTU::Engineering::Computer science and engineering
spellingShingle Mobile Security
Mobile Ad Hoc Networks
DRNTU::Engineering::Computer science and engineering
Jhaveri, Rutvij H.
Desai, Aneri
Patel, Ankit
Zhong, Yubin
A sequence number prediction based bait detection scheme to mitigate sequence number attacks in MANETs
description The characteristics of MANET such as decentralized architecture, dynamic topologies make MANETs susceptible to various security attacks. Sequence number attacks are such type of security threats which tend to degrade the network functioning and performance by sending fabricated route reply packets (RREP) with the objective of getting involved in the route and drop some or all of the data packets during the data transmission phase.The sequence number adversary attempts to send a fabricated high destination number in the RREP packet which attracts the sender to establish a path through the adversary node. This paper proposes a proactive secure routing mechanism which is an improvement over the authors previously proposed scheme. It makes use of linear regression mechanism to predict the maximum destination sequence number that the neighboring node can insert in the RREP packet. As an additional security checkpoint, it uses a bait detection mechanism to establish confidence in marking a suspicious node as a malicious node.The proposed approach works in collaboration with the ad hoc on-demand distance vector routing (AODV) protocol. The simulation results depict that the approach improves the network performance in the presence of adversaries as compared to previously proposed scheme.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Jhaveri, Rutvij H.
Desai, Aneri
Patel, Ankit
Zhong, Yubin
format Article
author Jhaveri, Rutvij H.
Desai, Aneri
Patel, Ankit
Zhong, Yubin
author_sort Jhaveri, Rutvij H.
title A sequence number prediction based bait detection scheme to mitigate sequence number attacks in MANETs
title_short A sequence number prediction based bait detection scheme to mitigate sequence number attacks in MANETs
title_full A sequence number prediction based bait detection scheme to mitigate sequence number attacks in MANETs
title_fullStr A sequence number prediction based bait detection scheme to mitigate sequence number attacks in MANETs
title_full_unstemmed A sequence number prediction based bait detection scheme to mitigate sequence number attacks in MANETs
title_sort sequence number prediction based bait detection scheme to mitigate sequence number attacks in manets
publishDate 2019
url https://hdl.handle.net/10356/105333
http://hdl.handle.net/10220/48081
https://doi.org/10.1155/2018/3210207
_version_ 1681042706280742912