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NeuDetect: a neural network data mining wireless network intrusion detection system

Published: 16 August 2010 Publication History

Abstract

This paper proposes NeuDetect, which applies a classification rule mining Neural Network technique to wireless network packets captured through hardware sensors for purposes of real time detection of anomalous packets. To address the problem of high false alarm rate confronted by current wireless intrusion detection systems, this paper presents a method of applying artificial neural networks mining classification technique to wireless network intrusion detection system. The proposed system, NeuDetect, solution approach is to find normal and anomalous patterns on pre-processed wireless packet records by comparing them with training data using Back-propagation algorithm.

References

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}}C. I. Ezeife, M. Ejelike, and A. K. Aggarwal. Wids: A sensor-based online mining wireless intrusion detection system. In Proceedings of the 12th ACM International Database Engineering & Applications Symposium (IDEAS08), pages 255--262. ACM, Sept 2008.
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}}J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, New York, 2000.
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}}Y. Liu, D. Tian, and B. Li. A wireless intrusion detection method based on dynamic growing neural network. In IEEE First International Multi-Symposiums on Computer and Computational Science - IMSCCSs, pages 611--615, June 2006.
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}}NetworkChemistry. Network chemistry wireless security business. http://www.networkchemistry.com, 2007.
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}}A. Rahman, C. I. Ezeife, and A. K. Aggarwal. Wifi miner: An online apriori-infrequent based wireless intrusion detection system. In Proceedings of the 2nd ACM SIGKDD '08 International Workshop on Knowledge Discovery from Sensor Data (Sensor-KDD, 2008), pages 77--89. ACM, August 2008.

Cited By

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  • (2018)A Rule Framed SVM Model for Classification of Various DDOS Attack in Distributed Network2018 2nd International Conference on Micro-Electronics and Telecommunication Engineering (ICMETE)10.1109/ICMETE.2018.00032(96-101)Online publication date: Sep-2018
  • (2013)A concept of behavioral reputation system in wireless networks2013 47th International Carnahan Conference on Security Technology (ICCST)10.1109/CCST.2013.6922057(1-5)Online publication date: Oct-2013

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Published In

cover image ACM Conferences
IDEAS '10: Proceedings of the Fourteenth International Database Engineering & Applications Symposium
August 2010
282 pages
ISBN:9781605589008
DOI:10.1145/1866480
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 August 2010

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Author Tags

  1. back-propagation algorithm
  2. hardware sensor
  3. neural network
  4. training data
  5. wi-fi
  6. wireless attacks
  7. wireless intrusion detection

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  • Research-article

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IDEAS '10
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  • ACM
  • Concordia University

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Overall Acceptance Rate 74 of 210 submissions, 35%

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Cited By

View all
  • (2018)A Rule Framed SVM Model for Classification of Various DDOS Attack in Distributed Network2018 2nd International Conference on Micro-Electronics and Telecommunication Engineering (ICMETE)10.1109/ICMETE.2018.00032(96-101)Online publication date: Sep-2018
  • (2013)A concept of behavioral reputation system in wireless networks2013 47th International Carnahan Conference on Security Technology (ICCST)10.1109/CCST.2013.6922057(1-5)Online publication date: Oct-2013

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