K. Mohanapriya1
, M. Savitha Devi2
Section:Research Paper, Product Type: Journal Paper
Volume-07 ,
Issue-17 , Page no. 43-46, May-2019
Online published on May 22, 2019
Copyright © K. Mohanapriya, M. Savitha Devi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Style Citation: K. Mohanapriya, M. Savitha Devi, “NIDS using Random Forest and Random Tree,” International Journal of Computer Sciences and Engineering, Vol.07, Issue.17, pp.43-46, 2019.
MLA Style Citation: K. Mohanapriya, M. Savitha Devi "NIDS using Random Forest and Random Tree." International Journal of Computer Sciences and Engineering 07.17 (2019): 43-46.
APA Style Citation: K. Mohanapriya, M. Savitha Devi, (2019). NIDS using Random Forest and Random Tree. International Journal of Computer Sciences and Engineering, 07(17), 43-46.
BibTex Style Citation:
@article{Mohanapriya_2019,
author = {K. Mohanapriya, M. Savitha Devi},
title = {NIDS using Random Forest and Random Tree},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2019},
volume = {07},
Issue = {17},
month = {5},
year = {2019},
issn = {2347-2693},
pages = {43-46},
url = {https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=1308},
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=1308
TI - NIDS using Random Forest and Random Tree
T2 - International Journal of Computer Sciences and Engineering
AU - K. Mohanapriya, M. Savitha Devi
PY - 2019
DA - 2019/05/22
PB - IJCSE, Indore, INDIA
SP - 43-46
IS - 17
VL - 07
SN - 2347-2693
ER -
Network Intrusion Detection Systems (NIDS) is the most important system in cyber security and it informs network administrators about policy violations. Identifying the network security violations and tells about where administrators to be improved. In Existing NIDS is designed to detect known network attacks. In this paper it is proposed to develop systematic methods for classifying intrusion detection. The key ideas are to use data mining techniques to discover network behaviour, anomalies and known Intrusions. Decision trees have been effectively used in NIDS but suffer from over sampling and the tree splitting being greedy locally. To overcome this some of the ensemble techniques like Random Forest, Random Trees and Ensemble Weak Learner Tree (EWL TREE) are used. Proposed technique reduces the number of trees required and also improves the precision and recall.
Intrusion Detection, Security, Intruder, Decision Tree, Ensemble Weak Learner
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