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Association Rule Pattern Mining Approaches Network Anomaly Detection
http://hdl.handle.net/20.500.12678/0000003112
http://hdl.handle.net/20.500.12678/000000311281e3a05f-5f3a-4b94-857c-41479599ec26
d00d9f87-46e1-4c4b-b7e3-6a2374a5ed63
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Conference paper | ||||||
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Publication | ||||||
Title | ||||||
Title | Association Rule Pattern Mining Approaches Network Anomaly Detection | |||||
Language | en | |||||
Publication date | 2015-03-30 | |||||
Authors | ||||||
Khin Moh Moh Aung | ||||||
Nyein Nyein Oo | ||||||
Description | ||||||
<p>The research area for intrusion detection is becoming growth with new challenges of attack day by<br> day. Intrusion detection system includes identifying a set of malicious actions that compromise the integrity,<br> confidentiality, and availability of information resources. The major objective of this paper is to apply<br> association rule pattern mining approaches for network intrusion detection system. In this paper, traditional FP-<br> growth algorithm, one of the association algorithms is modified and used to mine itemsets from large database.<br> The required statistics from large databases are gathered into a smaller data structure (FP-tree). The itemsets<br> generated from FP-tree are used as profiles to check anomaly detection in the proposed system.</p> |
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Keywords | ||||||
data mining, intrusion, anomaly, frequent itemset, algorithm | ||||||
Identifier | 10.5281/zenodo.3268373 | |||||
Journal articles | ||||||
Conference papers | ||||||
ICFCT'2015 | ||||||
29-30 March, 2015 | ||||||
Proceedings of 2015 International Conference on Future Computational Technologies (ICFCT'2015) | ||||||
Singapore | ||||||
Books/reports/chapters | ||||||
Thesis/dissertations |