A novel signature searching for Intrusion Detection System using data mining

Intrusion Detection System (IDS) has recently emerged as an important component for enhancing information system security. Data mining and machine learning technology has been extensively applied in network intrusion detection and prevention systems by discovering user behavior patterns from the net...

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Bibliographic Details
Published in2009 International Conference on Machine Learning and Cybernetics Vol. 1; pp. 122 - 126
Main Authors Ya-Li Ding, Lei Li, Hong-Qi Luo
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2009
Subjects
Apriori algorithm
Association rule
Association rules
Cybernetics
Data mining
Frequent itemset
Information systems
Intrusion detection
Itemsets
Machine learning
Machine learning algorithms
Pattern recognition
Protection
Scenario
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Summary:Intrusion Detection System (IDS) has recently emerged as an important component for enhancing information system security. Data mining and machine learning technology has been extensively applied in network intrusion detection and prevention systems by discovering user behavior patterns from the network traffic data. In this paper, we propose a novel signature searching to detect intrusion based on data mining, which is an improved Apriori algorithm. We evaluate the capability of this new approach with the data from KDD 1999 data mining competition. Our experimental results demonstrate the potential of the proposed method.
ISBN:9781424437023
1424437024
ISSN:2160-133X
DOI:10.1109/ICMLC.2009.5212577