Intruder Attack Detection In Data Network Organization Using Data Mining Techniques
Renu Dewli1 , Anubhooti Papola2
- Computer Science and Engineering, Faculty of Technology, Uttarakhand Technical University, Dehradun, India.
- Computer Science and Engineering, Faculty of Technology, Uttarakhand Technical University, Dehradun, India.
Section:Research Paper, Product Type: Journal Paper
Volume-6 ,
Issue-4 , Page no. 544-549, Apr-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6i4.544549
Online published on Apr 30, 2018
Copyright © Renu Dewli, Anubhooti Papola . 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: Renu Dewli, Anubhooti Papola, “Intruder Attack Detection In Data Network Organization Using Data Mining Techniques,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.544-549, 2018.
MLA Style Citation: Renu Dewli, Anubhooti Papola "Intruder Attack Detection In Data Network Organization Using Data Mining Techniques." International Journal of Computer Sciences and Engineering 6.4 (2018): 544-549.
APA Style Citation: Renu Dewli, Anubhooti Papola, (2018). Intruder Attack Detection In Data Network Organization Using Data Mining Techniques. International Journal of Computer Sciences and Engineering, 6(4), 544-549.
BibTex Style Citation:
@article{Dewli_2018,
author = {Renu Dewli, Anubhooti Papola},
title = {Intruder Attack Detection In Data Network Organization Using Data Mining Techniques},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {4 2018},
volume = {6},
Issue = {4},
month = {4},
year = {2018},
issn = {2347-2693},
pages = {544-549},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1898},
doi = {https://doi.org/10.26438/ijcse/v6i4.544549}
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i4.544549}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1898
TI - Intruder Attack Detection In Data Network Organization Using Data Mining Techniques
T2 - International Journal of Computer Sciences and Engineering
AU - Renu Dewli, Anubhooti Papola
PY - 2018
DA - 2018/04/30
PB - IJCSE, Indore, INDIA
SP - 544-549
IS - 4
VL - 6
SN - 2347-2693
ER -
VIEWS | XML | |
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Abstract
Networked data contain interconnected entities for which inferences are to be made. For example, web pages are interconnected by hyperlinks, research papers are associated by references, phone accounts are linked by calls, conceivable terrorists are linked by communications. Networks have turned out to be ubiquitous. Correspondence networks, financial transaction networks, networks portraying physical systems, and social networks are all ending up noticeably progressively important in our everyday life. Regularly, we are interested in models of how nodes in the system influence each other (for example, who taints whom in an epidemiological system), models for predicting an attribute of intrigue in light of observed attributes of objects in the system. The technique of SVM is applied which will classify the data into malicious and non-malicious. To increase the accuracy of classification technique Knn classier is applied which increase accuracy, execution time.
Key-Words / Index Term
Data network, attacks, data mining,, IDS/IPS machine learning
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