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

Performance Analysis of Anomaly based Intrusion Detection System using Modified FP-Growth Algorithm

http://hdl.handle.net/20.500.12678/0000003114
http://hdl.handle.net/20.500.12678/0000003114
683e013e-4d68-452a-aa3f-e599fc5073cd
d0ad1fa5-aaed-4474-b23b-9c3d061d5efd
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Performance Performance Analysis of Anomaly based Intrusion Detection System using Modified FP-Growth Algorithm.pdf (514 Kb)
Publication type
Conference paper
Upload type
Publication
Title
Title Performance Analysis of Anomaly based Intrusion Detection System using Modified FP-Growth Algorithm
Language en
Publication date 2015-12-12
Authors
Khin Moh Moh Aung
Nyein Aye Maung Maung
Description
<p>Nowadays,&nbsp; research&nbsp; area&nbsp; on&nbsp; network&nbsp; security&nbsp; is<br>
facing&nbsp; new&nbsp; challenges&nbsp; with&nbsp; the&nbsp; rapid&nbsp; increment&nbsp; of&nbsp; internet<br>
usage&nbsp; and&nbsp; possible&nbsp; attacks&nbsp; day&nbsp; by&nbsp; day.&nbsp; Intrusion&nbsp; detection<br>
technology&nbsp; is&nbsp; an&nbsp; effective&nbsp; approach&nbsp; to&nbsp; dealing&nbsp; with&nbsp; the<br>
problems&nbsp; of&nbsp; network&nbsp; security.&nbsp; In&nbsp; this&nbsp; paper,&nbsp; performance<br>
analysis&nbsp; of&nbsp; the&nbsp; proposed&nbsp; anomaly&nbsp; based&nbsp; intrusion&nbsp; detection<br>
system&nbsp; using&nbsp; modified&nbsp; FP-Growth&nbsp; algorithm&nbsp; is&nbsp; carried&nbsp; out<br>
based&nbsp; on&nbsp; several&nbsp; parameters.&nbsp; The&nbsp; proposed&nbsp; intrusion<br>
detection&nbsp; system&nbsp; is&nbsp; composed&nbsp; of&nbsp; three&nbsp; main&nbsp; parts:<br>
preprocessing,&nbsp; normal-attack&nbsp; detection&nbsp; and&nbsp; attack<br>
classification.&nbsp; KDD&nbsp; Cup&rsquo;99&nbsp; datasets&nbsp; are&nbsp; used&nbsp; as&nbsp; reference<br>
input&nbsp; dataset&nbsp; to&nbsp; experimentally&nbsp; analyze&nbsp; the&nbsp; performance&nbsp; of<br>
the&nbsp; system.&nbsp; Experimental&nbsp; results&nbsp; show&nbsp; that&nbsp; the&nbsp; proposed<br>
intrusion&nbsp; detection&nbsp; system&nbsp; offers&nbsp; preferable&nbsp; accuracy&nbsp; and<br>
false alarm rate while reducing the execution time.</p>
Keywords
FP-Growth algorithm, intrusion detection, data mining, anomaly, KDD-99 dataset
Identifier 10.5281/zenodo.3268391
Journal articles
Conference papers
ICSE
12-13 December, 2015
International Conference on Science and Engineering
Myanmar
Books/reports/chapters
Thesis/dissertations
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