Network intrusion detection based on neuro-fuzzy classification

Adel Nadjaran Toosi, Mohsen Kahani, Reza Monsefi

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

With rapid growth of computer networks during the past few years, network security has become a crucial issue. Among the various network security measures, intrusion detection systems (IDS) play a vital role to integrity, confidentiality and availability of resources. It seems that the presence of uncertainty and the imprecise nature of the intrusions make fuzzy systems suitable for such systems. Fuzzy systems are not normally adaptive and have not the ability to construct models solely based on the target system's sample data. One of the successful approaches which are incorporated fuzzy systems with adaptation and learning capabilities is the neural fuzzy method. The main objective of this work is to utilize ANFIS (Adaptive Neuro Fuzzy Inference System) as a classifier to detect intrusions in computer networks. This paper evaluates performance of ANFIS in the forms of binary and multi-classifier to categorize activities of a system into normal and suspicious or intrusive activities. Experiments for evaluation of the classifiers were performed with the KDD Cup 99 intrusion detection dataset. The Overall Results show that ANFIS can be effective in detecting various intrusions.

Original languageEnglish
Title of host publication2006 International Conference on Computing and Informatics, ICOCI '06
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages5
ISBN (Print)1424402204, 9781424402205
DOIs
Publication statusPublished - 2 Oct 2006
Externally publishedYes
Event2006 International Conference on Computing and Informatics, ICOCI '06 - Kuala Lumpur, Malaysia
Duration: 6 Jun 20068 Jun 2006

Conference

Conference2006 International Conference on Computing and Informatics, ICOCI '06
CountryMalaysia
CityKuala Lumpur
Period6/06/068/06/06

Keywords

  • ANFIS
  • Computer network security
  • Intrusion detection
  • KDD dataset
  • Neuro-Fuzzy classifier

Cite this

Toosi, A. N., Kahani, M., & Monsefi, R. (2006). Network intrusion detection based on neuro-fuzzy classification. In 2006 International Conference on Computing and Informatics, ICOCI '06 [5276608] IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICOCI.2006.5276608
Toosi, Adel Nadjaran ; Kahani, Mohsen ; Monsefi, Reza. / Network intrusion detection based on neuro-fuzzy classification. 2006 International Conference on Computing and Informatics, ICOCI '06. IEEE, Institute of Electrical and Electronics Engineers, 2006.
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Toosi, AN, Kahani, M & Monsefi, R 2006, Network intrusion detection based on neuro-fuzzy classification. in 2006 International Conference on Computing and Informatics, ICOCI '06., 5276608, IEEE, Institute of Electrical and Electronics Engineers, 2006 International Conference on Computing and Informatics, ICOCI '06, Kuala Lumpur, Malaysia, 6/06/06. https://doi.org/10.1109/ICOCI.2006.5276608

Network intrusion detection based on neuro-fuzzy classification. / Toosi, Adel Nadjaran; Kahani, Mohsen; Monsefi, Reza.

2006 International Conference on Computing and Informatics, ICOCI '06. IEEE, Institute of Electrical and Electronics Engineers, 2006. 5276608.

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

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N2 - With rapid growth of computer networks during the past few years, network security has become a crucial issue. Among the various network security measures, intrusion detection systems (IDS) play a vital role to integrity, confidentiality and availability of resources. It seems that the presence of uncertainty and the imprecise nature of the intrusions make fuzzy systems suitable for such systems. Fuzzy systems are not normally adaptive and have not the ability to construct models solely based on the target system's sample data. One of the successful approaches which are incorporated fuzzy systems with adaptation and learning capabilities is the neural fuzzy method. The main objective of this work is to utilize ANFIS (Adaptive Neuro Fuzzy Inference System) as a classifier to detect intrusions in computer networks. This paper evaluates performance of ANFIS in the forms of binary and multi-classifier to categorize activities of a system into normal and suspicious or intrusive activities. Experiments for evaluation of the classifiers were performed with the KDD Cup 99 intrusion detection dataset. The Overall Results show that ANFIS can be effective in detecting various intrusions.

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Toosi AN, Kahani M, Monsefi R. Network intrusion detection based on neuro-fuzzy classification. In 2006 International Conference on Computing and Informatics, ICOCI '06. IEEE, Institute of Electrical and Electronics Engineers. 2006. 5276608 https://doi.org/10.1109/ICOCI.2006.5276608