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  1. Yangon Technological University
  2. Department of Computer Engineering and Information Technology

Association Rule Pattern Mining Approaches Network Anomaly Detection

http://hdl.handle.net/20.500.12678/0000003112
http://hdl.handle.net/20.500.12678/0000003112
81e3a05f-5f3a-4b94-857c-41479599ec26
d00d9f87-46e1-4c4b-b7e3-6a2374a5ed63
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Association Association Rule Pattern Mining Approaches Network Anomaly Detection.pdf (219 Kb)
Publication type
Conference paper
Upload type
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.&nbsp; Intrusion&nbsp; detection&nbsp; system&nbsp; includes&nbsp; identifying&nbsp; a&nbsp; set&nbsp; of&nbsp; malicious&nbsp; actions&nbsp; that&nbsp; compromise&nbsp; the&nbsp; integrity,<br>
confidentiality,&nbsp; and&nbsp; availability&nbsp; of&nbsp; information&nbsp; resources.&nbsp; The&nbsp; major&nbsp; objective&nbsp; of&nbsp; this&nbsp; paper&nbsp; is&nbsp; to&nbsp; 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>
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
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