Intrusion detection based on the semi-supervised Fuzzy C-Means clustering algorithm

The intrusion detection algorithm based on the supervised learning has a high detection rate, but all the labeled data which hard to collect are needed when the algorithm used. Meanwhile the intrusion detection algorithm based on the unsupervised learning has a high False Positive Rate. In this pape...

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Published in:2012 2nd International Conference on Consumer Electronics, Communications and Networks pp. 2667 - 2670
Main Authors: Feng Guorui, Zou Xinguo, Wu Jian
Format: Conference Proceeding
Language:English
Published: IEEE 01.04.2012
Subjects:
Algorithm design and analysis
Charge carrier processes
Clustering algorithms
Decision support systems
FCM
Hafnium compounds
Intrusion detection
KDD CUP 99
semi-supervised
Zirconium
ISBN:9781457714146, 1457714140
Online Access:Get full text
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Abstract The intrusion detection algorithm based on the supervised learning has a high detection rate, but all the labeled data which hard to collect are needed when the algorithm used. Meanwhile the intrusion detection algorithm based on the unsupervised learning has a high False Positive Rate. In this paper a semi-supervised learning algorithm for intrusion detection is proposed combined with the Fuzzy C-Means algorithm. The sensitivity to the initial values and the probability of trapping in local optimum are greatly reduced by using few labeled data to improve the learning ability of the FCM algorithm. The KDD CUP99 data set is adopted as the experimental subject. The result proves that the attack behaviors can be more efficiently found from the network data by the semi-supervised FCM clustering algorithm.
AbstractList The intrusion detection algorithm based on the supervised learning has a high detection rate, but all the labeled data which hard to collect are needed when the algorithm used. Meanwhile the intrusion detection algorithm based on the unsupervised learning has a high False Positive Rate. In this paper a semi-supervised learning algorithm for intrusion detection is proposed combined with the Fuzzy C-Means algorithm. The sensitivity to the initial values and the probability of trapping in local optimum are greatly reduced by using few labeled data to improve the learning ability of the FCM algorithm. The KDD CUP99 data set is adopted as the experimental subject. The result proves that the attack behaviors can be more efficiently found from the network data by the semi-supervised FCM clustering algorithm.
Author Zou Xinguo
Wu Jian
Feng Guorui
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  surname: Zou Xinguo
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  surname: Wu Jian
  fullname: Wu Jian
  organization: Dept. of Inf. Sci. & Technol., Shandong Univ. of Political Sci. & Law, Jinan, China
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Snippet The intrusion detection algorithm based on the supervised learning has a high detection rate, but all the labeled data which hard to collect are needed when...
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SubjectTerms Algorithm design and analysis
Charge carrier processes
Clustering algorithms
Decision support systems
FCM
Hafnium compounds
Intrusion detection
KDD CUP 99
semi-supervised
Zirconium
Title Intrusion detection based on the semi-supervised Fuzzy C-Means clustering algorithm
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