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Reseach Article

A Hybrid Data Mining Model for Intrusion Detection

by Mahreen Nasir
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 16
Year of Publication: 2021
Authors: Mahreen Nasir
10.5120/ijca2021921489

Mahreen Nasir . A Hybrid Data Mining Model for Intrusion Detection. International Journal of Computer Applications. 183, 16 ( Jul 2021), 14-19. DOI=10.5120/ijca2021921489

@article{ 10.5120/ijca2021921489,
author = { Mahreen Nasir },
title = { A Hybrid Data Mining Model for Intrusion Detection },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2021 },
volume = { 183 },
number = { 16 },
month = { Jul },
year = { 2021 },
issn = { 0975-8887 },
pages = { 14-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number16/32009-2021921489/ },
doi = { 10.5120/ijca2021921489 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:16:58.084143+05:30
%A Mahreen Nasir
%T A Hybrid Data Mining Model for Intrusion Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 16
%P 14-19
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Network intrusion detection requires analysis of network data streams for identification of possible attacks. An Intrusion Detection System (IDS) is used to analyse such attacks and prevent future attacks. Main categories of IDS are anomaly detection and misuse detection. The limitation of anomaly based detection is high false positive rate whereas misuse detection based systems can only deal with known attack types. To address these, the main contribution of this paper is to propose a framework using hybrid approach based on clustering and classification methods for Intrusion Detection (CCID).

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Index Terms

Computer Science
Information Sciences

Keywords

Intrusion Detection Classification Clustering Data Mining