Analyzing attacks in wireless ad hoc network with self-organizing maps

Traian Avram; Seungchan Oh; Salim Hariri

doi:10.1109/CNSR.2007.15

Analyzing attacks in wireless ad hoc network with self-organizing maps

Traian Avram, Seungchan Oh, Salim Hariri

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

15 Scopus citations

Abstract

Detecting anomalous events and attacks in the ad-hoc wireless network is a challenging area for research due to the characteristics of wireless network. The proposed detection system monitors network traffic on each node and analyzes collected data by Self-Organizing Maps to extract statistical regularities from the input data vectors and encode them into the weights without supervision. We evaluate our approach to detect network attacks on AODV and DSR protocols using OPNET. Our simulation results show that our approach can accurately detect anomalous behaviors caused by network attacks.

Original language	English (US)
Title of host publication	Proceedings - CNSR 2007
Subtitle of host publication	Fifth Annual Conference on Communication Networks and Services Research
Pages	166-175
Number of pages	10
DOIs	https://doi.org/10.1109/CNSR.2007.15
State	Published - 2007
Event	CNSR 2007: 5th Annual Conference on Communication Networks and Services Research - Fredericton, NB, Canada Duration: May 14 2007 → May 17 2007

Publication series

Name	Proceedings - CNSR 2007: Fifth Annual Conference on Communication Networks and Services Research

Other

Other	CNSR 2007: 5th Annual Conference on Communication Networks and Services Research
Country/Territory	Canada
City	Fredericton, NB
Period	5/14/07 → 5/17/07

Keywords

Computer network security
MANET
Self-organizing feature maps
Site security monitoring

ASJC Scopus subject areas

Computer Networks and Communications
Software

Access to Document

10.1109/CNSR.2007.15

Cite this

Avram, T., Oh, S., & Hariri, S. (2007). Analyzing attacks in wireless ad hoc network with self-organizing maps. In Proceedings - CNSR 2007: Fifth Annual Conference on Communication Networks and Services Research (pp. 166-175). Article 4215510 (Proceedings - CNSR 2007: Fifth Annual Conference on Communication Networks and Services Research). https://doi.org/10.1109/CNSR.2007.15

Analyzing attacks in wireless ad hoc network with self-organizing maps. / Avram, Traian; Oh, Seungchan; Hariri, Salim.
Proceedings - CNSR 2007: Fifth Annual Conference on Communication Networks and Services Research. 2007. p. 166-175 4215510 (Proceedings - CNSR 2007: Fifth Annual Conference on Communication Networks and Services Research).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Avram, T, Oh, S & Hariri, S 2007, Analyzing attacks in wireless ad hoc network with self-organizing maps. in Proceedings - CNSR 2007: Fifth Annual Conference on Communication Networks and Services Research., 4215510, Proceedings - CNSR 2007: Fifth Annual Conference on Communication Networks and Services Research, pp. 166-175, CNSR 2007: 5th Annual Conference on Communication Networks and Services Research, Fredericton, NB, Canada, 5/14/07. https://doi.org/10.1109/CNSR.2007.15

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abstract = "Detecting anomalous events and attacks in the ad-hoc wireless network is a challenging area for research due to the characteristics of wireless network. The proposed detection system monitors network traffic on each node and analyzes collected data by Self-Organizing Maps to extract statistical regularities from the input data vectors and encode them into the weights without supervision. We evaluate our approach to detect network attacks on AODV and DSR protocols using OPNET. Our simulation results show that our approach can accurately detect anomalous behaviors caused by network attacks.",

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AB - Detecting anomalous events and attacks in the ad-hoc wireless network is a challenging area for research due to the characteristics of wireless network. The proposed detection system monitors network traffic on each node and analyzes collected data by Self-Organizing Maps to extract statistical regularities from the input data vectors and encode them into the weights without supervision. We evaluate our approach to detect network attacks on AODV and DSR protocols using OPNET. Our simulation results show that our approach can accurately detect anomalous behaviors caused by network attacks.

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Analyzing attacks in wireless ad hoc network with self-organizing maps

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

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