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Detection of DDoS Attack Using UCLA Dataset on Different Classifiers

Aakriti Aggarwal1 , Ankur Gupta2

Section:Research Paper, Product Type: Journal Paper
Volume-3 , Issue-8 , Page no. 33-36, Aug-2015

Online published on Aug 31, 2015

Copyright © Aakriti Aggarwal , Ankur Gupta . 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: Aakriti Aggarwal , Ankur Gupta, “Detection of DDoS Attack Using UCLA Dataset on Different Classifiers,” International Journal of Computer Sciences and Engineering, Vol.3, Issue.8, pp.33-36, 2015.

MLA Style Citation: Aakriti Aggarwal , Ankur Gupta "Detection of DDoS Attack Using UCLA Dataset on Different Classifiers." International Journal of Computer Sciences and Engineering 3.8 (2015): 33-36.

APA Style Citation: Aakriti Aggarwal , Ankur Gupta, (2015). Detection of DDoS Attack Using UCLA Dataset on Different Classifiers. International Journal of Computer Sciences and Engineering, 3(8), 33-36.

BibTex Style Citation:
@article{Aggarwal_2015,
author = {Aakriti Aggarwal , Ankur Gupta},
title = {Detection of DDoS Attack Using UCLA Dataset on Different Classifiers},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {8 2015},
volume = {3},
Issue = {8},
month = {8},
year = {2015},
issn = {2347-2693},
pages = {33-36},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=604},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=604
TI - Detection of DDoS Attack Using UCLA Dataset on Different Classifiers
T2 - International Journal of Computer Sciences and Engineering
AU - Aakriti Aggarwal , Ankur Gupta
PY - 2015
DA - 2015/08/31
PB - IJCSE, Indore, INDIA
SP - 33-36
IS - 8
VL - 3
SN - 2347-2693
ER -

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Abstract

Distributed denial of service attack have strong Impact on security of internet because these attacks affects the normal functioning causing loss of billions of dollars. DDoS is very harmful to network as it delays the legitimate users from excessing the server. However these networks were well equipped in security yet they were damaged by DDoS attack. In this paper, the proposed system presents both detecting and classifying schemes of DDoS attack using K-NN, SVM and Naïve Bayesian. The algorithms are developed by using various features of attack packets. By studying the incoming and outgoing network traffic and different classifiers are used to analyze these features. The main objective of this paper is to study classifiers on one dataset for DDoS attack.

Key-Words / Index Term

DDoS attack, Internet Securities, Attack Packet

References

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