Identifying and Locating Multiple Spoofing Attackers Using Clustering in Wireless Network

AMALA GRACY; CHINNAPPAN JAYAKUMAR

doi:doi:10.11648/j.wcmc.20130104.11

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Identifying and Locating Multiple Spoofing Attackers Using Clustering in Wireless Network

AMALA GRACY, CHINNAPPAN JAYAKUMAR

Published in International Journal of Wireless Communications and Mobile Computing (Volume 1, Issue 4)

Received: 2 September 2013 Accepted: Published: 30 September 2013

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Abstract

Wireless networks are vulnerable to identity-based attacks, including spoofing attacks, significantly impact the performance of networks. Conventionally, ensuring the identity of the communicator and detecting an adversarial presence is performed via cryptographic authentication. Unfortunately, full-scale authentication is not always desirable as it requires key management, coupled with additional infrastructural overhead and more extensive computations. The proposed non cryptographic mechanism which are complementary to authenticate and can detect device spoofing with little or no dependency on cryptographic keys. This generalized Spoofing attack-detection model utilizes MD5 (Message Digest 5) algorithm to generate unique identifier for each wireless nodes and a physical property associated with each node, as the basis for (1) detecting spoofing attacks; (2) finding the number of attackers when multiple adversaries masquerading as a same node identity; and localizing multiple adversaries. Cluster-based mechanisms are developed to determine the number of attackers. The proposed model can be explored further to improve the accuracy of determining the number of attackers, by using Support Vector Machines (SVM).

Published in	International Journal of Wireless Communications and Mobile Computing (Volume 1, Issue 4)
DOI	10.11648/j.wcmc.20130104.11
Page(s)	82-90
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Wireless Network, Spoofing Attack, Identity-Based Attack, Message Digest 5, Support Vector Machines, Partitioning Around Medoids (PAM) Cluster Model

References

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APA Style

AMALA GRACY, CHINNAPPAN JAYAKUMAR. (2013). Identifying and Locating Multiple Spoofing Attackers Using Clustering in Wireless Network. International Journal of Wireless Communications and Mobile Computing, 1(4), 82-90. https://doi.org/10.11648/j.wcmc.20130104.11

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ACS Style

AMALA GRACY; CHINNAPPAN JAYAKUMAR. Identifying and Locating Multiple Spoofing Attackers Using Clustering in Wireless Network. Int. J. Wirel. Commun. Mobile Comput. 2013, 1(4), 82-90. doi: 10.11648/j.wcmc.20130104.11

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AMA Style

AMALA GRACY, CHINNAPPAN JAYAKUMAR. Identifying and Locating Multiple Spoofing Attackers Using Clustering in Wireless Network. Int J Wirel Commun Mobile Comput. 2013;1(4):82-90. doi: 10.11648/j.wcmc.20130104.11

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@article{10.11648/j.wcmc.20130104.11,
  author = {AMALA GRACY and CHINNAPPAN JAYAKUMAR},
  title = {Identifying and Locating Multiple Spoofing Attackers Using Clustering in Wireless Network},
  journal = {International Journal of Wireless Communications and Mobile Computing},
  volume = {1},
  number = {4},
  pages = {82-90},
  doi = {10.11648/j.wcmc.20130104.11},
  url = {https://doi.org/10.11648/j.wcmc.20130104.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.wcmc.20130104.11},
  abstract = {Wireless networks are vulnerable to identity-based attacks, including spoofing attacks, significantly impact the performance of networks.  Conventionally, ensuring the identity of the communicator and detecting an adversarial presence is performed via cryptographic authentication. Unfortunately, full-scale authentication is not always desirable as it requires key management, coupled with additional infrastructural overhead and more extensive computations. The proposed non cryptographic mechanism which are complementary to authenticate and can detect device spoofing with little or no dependency on cryptographic keys. This generalized Spoofing attack-detection model utilizes MD5 (Message Digest 5) algorithm to generate unique identifier for each wireless nodes and a physical property associated with each node, as the basis for (1) detecting spoofing attacks; (2) finding the number of attackers when multiple adversaries masquerading as a same node identity; and localizing multiple adversaries. Cluster-based mechanisms are developed to determine the number of attackers.  The proposed model can be explored further to improve the accuracy of determining the number of attackers, by using Support Vector Machines (SVM).},
 year = {2013}
}

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TY  - JOUR
T1  - Identifying and Locating Multiple Spoofing Attackers Using Clustering in Wireless Network
AU  - AMALA GRACY
AU  - CHINNAPPAN JAYAKUMAR
Y1  - 2013/09/30
PY  - 2013
N1  - https://doi.org/10.11648/j.wcmc.20130104.11
DO  - 10.11648/j.wcmc.20130104.11
T2  - International Journal of Wireless Communications and Mobile Computing
JF  - International Journal of Wireless Communications and Mobile Computing
JO  - International Journal of Wireless Communications and Mobile Computing
SP  - 82
EP  - 90
PB  - Science Publishing Group
SN  - 2330-1015
UR  - https://doi.org/10.11648/j.wcmc.20130104.11
AB  - Wireless networks are vulnerable to identity-based attacks, including spoofing attacks, significantly impact the performance of networks.  Conventionally, ensuring the identity of the communicator and detecting an adversarial presence is performed via cryptographic authentication. Unfortunately, full-scale authentication is not always desirable as it requires key management, coupled with additional infrastructural overhead and more extensive computations. The proposed non cryptographic mechanism which are complementary to authenticate and can detect device spoofing with little or no dependency on cryptographic keys. This generalized Spoofing attack-detection model utilizes MD5 (Message Digest 5) algorithm to generate unique identifier for each wireless nodes and a physical property associated with each node, as the basis for (1) detecting spoofing attacks; (2) finding the number of attackers when multiple adversaries masquerading as a same node identity; and localizing multiple adversaries. Cluster-based mechanisms are developed to determine the number of attackers.  The proposed model can be explored further to improve the accuracy of determining the number of attackers, by using Support Vector Machines (SVM).
VL  - 1
IS  - 4
ER  -

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Author Information

AMALA GRACY

Department of Information Technology, RMK Engineering College, Anna University, Chennai, INDIA
CHINNAPPAN JAYAKUMAR

Department of Computer Science and Engineering, RMK Engineering College, Anna University, Chennai, INDIA

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Plain Text BibTeX RIS

APA Style

AMALA GRACY, CHINNAPPAN JAYAKUMAR. (2013). Identifying and Locating Multiple Spoofing Attackers Using Clustering in Wireless Network. International Journal of Wireless Communications and Mobile Computing, 1(4), 82-90. https://doi.org/10.11648/j.wcmc.20130104.11

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ACS Style

AMALA GRACY; CHINNAPPAN JAYAKUMAR. Identifying and Locating Multiple Spoofing Attackers Using Clustering in Wireless Network. Int. J. Wirel. Commun. Mobile Comput. 2013, 1(4), 82-90. doi: 10.11648/j.wcmc.20130104.11

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AMA Style

AMALA GRACY, CHINNAPPAN JAYAKUMAR. Identifying and Locating Multiple Spoofing Attackers Using Clustering in Wireless Network. Int J Wirel Commun Mobile Comput. 2013;1(4):82-90. doi: 10.11648/j.wcmc.20130104.11

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@article{10.11648/j.wcmc.20130104.11,
  author = {AMALA GRACY and CHINNAPPAN JAYAKUMAR},
  title = {Identifying and Locating Multiple Spoofing Attackers Using Clustering in Wireless Network},
  journal = {International Journal of Wireless Communications and Mobile Computing},
  volume = {1},
  number = {4},
  pages = {82-90},
  doi = {10.11648/j.wcmc.20130104.11},
  url = {https://doi.org/10.11648/j.wcmc.20130104.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.wcmc.20130104.11},
  abstract = {Wireless networks are vulnerable to identity-based attacks, including spoofing attacks, significantly impact the performance of networks.  Conventionally, ensuring the identity of the communicator and detecting an adversarial presence is performed via cryptographic authentication. Unfortunately, full-scale authentication is not always desirable as it requires key management, coupled with additional infrastructural overhead and more extensive computations. The proposed non cryptographic mechanism which are complementary to authenticate and can detect device spoofing with little or no dependency on cryptographic keys. This generalized Spoofing attack-detection model utilizes MD5 (Message Digest 5) algorithm to generate unique identifier for each wireless nodes and a physical property associated with each node, as the basis for (1) detecting spoofing attacks; (2) finding the number of attackers when multiple adversaries masquerading as a same node identity; and localizing multiple adversaries. Cluster-based mechanisms are developed to determine the number of attackers.  The proposed model can be explored further to improve the accuracy of determining the number of attackers, by using Support Vector Machines (SVM).},
 year = {2013}
}

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TY  - JOUR
T1  - Identifying and Locating Multiple Spoofing Attackers Using Clustering in Wireless Network
AU  - AMALA GRACY
AU  - CHINNAPPAN JAYAKUMAR
Y1  - 2013/09/30
PY  - 2013
N1  - https://doi.org/10.11648/j.wcmc.20130104.11
DO  - 10.11648/j.wcmc.20130104.11
T2  - International Journal of Wireless Communications and Mobile Computing
JF  - International Journal of Wireless Communications and Mobile Computing
JO  - International Journal of Wireless Communications and Mobile Computing
SP  - 82
EP  - 90
PB  - Science Publishing Group
SN  - 2330-1015
UR  - https://doi.org/10.11648/j.wcmc.20130104.11
AB  - Wireless networks are vulnerable to identity-based attacks, including spoofing attacks, significantly impact the performance of networks.  Conventionally, ensuring the identity of the communicator and detecting an adversarial presence is performed via cryptographic authentication. Unfortunately, full-scale authentication is not always desirable as it requires key management, coupled with additional infrastructural overhead and more extensive computations. The proposed non cryptographic mechanism which are complementary to authenticate and can detect device spoofing with little or no dependency on cryptographic keys. This generalized Spoofing attack-detection model utilizes MD5 (Message Digest 5) algorithm to generate unique identifier for each wireless nodes and a physical property associated with each node, as the basis for (1) detecting spoofing attacks; (2) finding the number of attackers when multiple adversaries masquerading as a same node identity; and localizing multiple adversaries. Cluster-based mechanisms are developed to determine the number of attackers.  The proposed model can be explored further to improve the accuracy of determining the number of attackers, by using Support Vector Machines (SVM).
VL  - 1
IS  - 4
ER  -

Copy | Download