Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Single Password Authentication Protocol Read chapter Read first chapter

2017 | OriginalPaper | Chapter

A Novel Anomaly Detection Method in Wireless Network Using Multi-level Classifier Ensembles

Authors : Bayu Adhi Tama, Kyung-Hyune Rhee

Published in: Advanced Multimedia and Ubiquitous Engineering

Publisher: Springer Singapore

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Anomaly detection is very crucial in an intrusion detection task since it has capability to discover new types of attacks. The major challenges of anomaly detection are how to maximize the accuracy while maintaining low positive rate. In this paper, we propose new approach on anomaly detection using multi-level classifier ensembles. We employ an ensemble learner as a base classifier of ensemble rather than a single classifier algorithm. We run several experiments to choose the best combination of two-level classifier ensemble model. From our experimental result, it is revealed that the performance of our proposed approach yields satisfactory results over classical classifier ensembles and single classifiers.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Wearable and Portable Monitoring Devices for Older People
next chapter Learning Reaction Analysis Engine for Interactive Digital Textbook Platform
Literature
1.
Tama, B., Rhee, K.: Performance analysis of multiple classifier system in DoS attack detection. In: Information Security Applications, vol. 9503. Springer, Heidelberg (2016)
2.
Tama, B., Rhee, K.: A combination of PSO-based feature selection and tree-based classifiers ensemble for intrusion detection systems. In: Advances in Computer Science and Ubiquitous Computing, pp. 489–495. Springer, Singapore (2015)
3.
Mukkamala, S., Sung, A., Abraham, A.: Intrusion detection using an ensemble of intelligent paradigms. J. Netw. Comput. Appl. 28(2), 167–182 (2005)CrossRef
4.
Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.: A detailed analysis of the KDD CUP 99 data set. In: The Second IEEE Symposium on Computational Intelligence for Security and Defence Applications (2009)
5.
Vilela, D., Ferreira, E., Shinoda, A., de Souza Araujo, N., de Oliveira, R., Nascimento, V.: A dataset for evaluating intrusion detection systems in IEEE 802.11 wireless networks. In: IEEE Colombian Conference on Communications and Computing (COLCOM), pp. 1–5 (2014)
6.
Rodriguez, J., Kuncheva, L., Alonso, C.: Rotation forest: a new classifier ensemble method. IEEE Trans. Pattern Anal. Mach. Intell. 28(10), 1619–1630 (2006)CrossRef
7.
Tama, B., Rhee, K.: classifier ensemble design with rotation forest to enhance attack detection of IDS in wireless network. In: 11th Asia Joint Conference on Information Security (AsiaJCIS), Fukuoka, pp. 87–91 (2016)
8.
Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Ann. Stat. 28(2), 337–407 (2000)MathSciNetCrossRefMATH
9.
Friedman, M.: A comparison of alternative tests of significance for the problem of m rankings. Ann. Math. Stat. 11(1), 86–92 (1940)MathSciNetCrossRefMATH
10.
Nemenyi, P.: Distribution-free multiple comparisons. Biometrics 18(2), 263 (1962)
11.
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)MathSciNetMATH
12.
Japkowicz, N., Shah, M.: Evaluating Learning Algorithms: A Classification Perspective. Cambridge University Press, Cambridge (2011)CrossRefMATH
Metadata
Title
A Novel Anomaly Detection Method in Wireless Network Using Multi-level Classifier Ensembles
Authors
Bayu Adhi Tama
Kyung-Hyune Rhee
Copyright Year
2017
Publisher
Springer Singapore
Book
Advanced Multimedia and Ubiquitous Engineering

Print ISBN: 978-981-10-5040-4

Electronic ISBN: 978-981-10-5041-1

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-981-10-5041-1_73

Premium Partner

    Image Credits