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Fifth Annual Conference on Communication Networks and Services Research (CNSR '07)
Using Neuro-Fuzzy Approach to Reduce False Positive Alerts
Fredericton, New Brunswick, Canada
May 14-May 17
ISBN: 0-7695-2835-X
ASCII Text
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Riyad Alshammari, Sumalee Sonamthiang, Mohsen Teimouri, Denis Riordan, "Using Neuro-Fuzzy Approach to Reduce False Positive Alerts," Communication Networks and Services Research, Annual Conference on, pp. 345-349, Fifth Annual Conference on Communication Networks and Services Research (CNSR '07), 2007.
 
 
BibTex
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@article{ 10.1109/CNSR.2007.70,
author = {Riyad Alshammari and Sumalee Sonamthiang and Mohsen Teimouri and Denis Riordan},
title = {Using Neuro-Fuzzy Approach to Reduce False Positive Alerts},
journal ={Communication Networks and Services Research, Annual Conference on},
volume = {0},
year = {2007},
isbn = {0-7695-2835-X},
pages = {345-349},
doi = {http://doi.ieeecomputersociety.org/10.1109/CNSR.2007.70},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Communication Networks and Services Research, Annual Conference on
TI - Using Neuro-Fuzzy Approach to Reduce False Positive Alerts
SN - 0-7695-2835-X
SP345
EP349
A1 - Riyad Alshammari,
A1 - Sumalee Sonamthiang,
A1 - Mohsen Teimouri,
A1 - Denis Riordan,
PY - 2007
KW - Intrusion Detection
KW - False Positive
KW - Neuro- Fuzzy
KW - Classification
KW - Security
VL - 0
JA - Communication Networks and Services Research, Annual Conference on
ER -
 
Riyad Alshammari, Dalhousie University, Canada
Sumalee Sonamthiang, Dalhousie University, Canada
Mohsen Teimouri, Dalhousie University, Canada
Denis Riordan, Dalhousie University, Canada
One of the major problems of Intrusion Detection Systems (IDS) at the present is the high rate of false alerts that the systems produce. These alerts cause problems to human analysts to repeatedly and intensively analyze the false alerts to initiate appropriate actions. We demonstrate the advantages of using a hybrid neuro-fuzzy approach to reduce the number of false alarms. The neuro-fuzzy approach was experimented with different background knowledge sets in DARPA 1999 network traffic dataset. The approach was evaluated and compared with RIPPER algorithm. The results shows that the neurofuzzy approach significantly reduces the number of false alarms more than the RIPPER algorithm and requires less background knowledge sets.
Index Terms:
Intrusion Detection, False Positive, Neuro- Fuzzy, Classification, Security
Citation:
Riyad Alshammari, Sumalee Sonamthiang, Mohsen Teimouri, Denis Riordan, "Using Neuro-Fuzzy Approach to Reduce False Positive Alerts," cnsr, pp.345-349, Fifth Annual Conference on Communication Networks and Services Research (CNSR '07), 2007
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