Impact of Baseline Profile on Intrusion Detection in Mobile Ad Hoc Networks

Binh Hy Dang; Wei Li

doi:10.1109/SECON.2015.7133013

Impact of Baseline Profile on Intrusion Detection in Mobile Ad Hoc Networks

Binh Hy Dang
, Wei Li

Nova Southeastern University

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

Abstract

Dynamic topology and limited resources are major limitations that make intrusion detection in mobile ad hoc network (MANET) a difficult task. In recent years, several anomaly detection techniques were proposed to detect malicious nodes using static and dynamic baseline profiles, which depict normal MANET behaviors. In this research, we investigated different baseline profile methods and conducted a set of experiments to evaluate their effectiveness and efficiency for anomaly detection in MANETs using C-means clustering technique. The results indicated that a static baseline profile delivers similar results to other baseline profile methods. However, it requires the least resource usage while a dynamic baseline profile method requires the most resource usage of all the baseline models.

Original language	American English
Title of host publication	IEEE SoutheastCon 2015 - Conference Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Edition	June
ISBN (Electronic)	9781467373005
DOIs	https://doi.org/10.1109/SECON.2015.7133013
State	Published - Jun 24 2015
Event	IEEE SoutheastCon 2015 - Fort Lauderdale, United States Duration: Apr 9 2015 → Apr 12 2015

Publication series

Name	Conference Proceedings - IEEE SOUTHEASTCON
Number	June
Volume	2015-June
ISSN (Print)	1091-0050
ISSN (Electronic)	1558-058X

Conference

Conference	IEEE SoutheastCon 2015
Country/Territory	United States
City	Fort Lauderdale
Period	4/9/15 → 4/12/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

ASJC Scopus Subject Areas

Computer Networks and Communications
Software
Electrical and Electronic Engineering
Control and Systems Engineering
Signal Processing

Keywords

anomaly detection
baseline profile
clustering technique
Mobile ad hoc networks
unsupervised learning techniques

Access to Document

10.1109/SECON.2015.7133013

Cite this

Impact of Baseline Profile on Intrusion Detection in Mobile Ad Hoc Networks. / Dang, Binh Hy; Li, Wei.
IEEE SoutheastCon 2015 - Conference Proceedings. June. ed. Institute of Electrical and Electronics Engineers Inc., 2015. 7133013 (Conference Proceedings - IEEE SOUTHEASTCON; Vol. 2015-June, No. June).

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

Dang, BH & Li, W 2015, Impact of Baseline Profile on Intrusion Detection in Mobile Ad Hoc Networks. in IEEE SoutheastCon 2015 - Conference Proceedings. June edn, 7133013, Conference Proceedings - IEEE SOUTHEASTCON, no. June, vol. 2015-June, Institute of Electrical and Electronics Engineers Inc., IEEE SoutheastCon 2015, Fort Lauderdale, United States, 4/9/15. https://doi.org/10.1109/SECON.2015.7133013

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abstract = "Dynamic topology and limited resources are major limitations that make intrusion detection in mobile ad hoc network (MANET) a difficult task. In recent years, several anomaly detection techniques were proposed to detect malicious nodes using static and dynamic baseline profiles, which depict normal MANET behaviors. In this research, we investigated different baseline profile methods and conducted a set of experiments to evaluate their effectiveness and efficiency for anomaly detection in MANETs using C-means clustering technique. The results indicated that a static baseline profile delivers similar results to other baseline profile methods. However, it requires the least resource usage while a dynamic baseline profile method requires the most resource usage of all the baseline models.",

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AB - Dynamic topology and limited resources are major limitations that make intrusion detection in mobile ad hoc network (MANET) a difficult task. In recent years, several anomaly detection techniques were proposed to detect malicious nodes using static and dynamic baseline profiles, which depict normal MANET behaviors. In this research, we investigated different baseline profile methods and conducted a set of experiments to evaluate their effectiveness and efficiency for anomaly detection in MANETs using C-means clustering technique. The results indicated that a static baseline profile delivers similar results to other baseline profile methods. However, it requires the least resource usage while a dynamic baseline profile method requires the most resource usage of all the baseline models.

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KW - clustering technique

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KW - unsupervised learning techniques

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BT - IEEE SoutheastCon 2015 - Conference Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

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Impact of Baseline Profile on Intrusion Detection in Mobile Ad Hoc Networks

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus Subject Areas

Keywords

Access to Document

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Cite this