UGC Approved Journal no 63975

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 6 Issue 6
June-2019
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

7.95 impact factor calculated by Google scholar

Unique Identifier

Published Paper ID:
JETIR1908B09


Registration ID:
227077

Page Number

59-64

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Title

MACHINE LEARNING TECHNIQUES FOR INTRUSION DETECTION

Abstract

In cyber security, intrusion detection is the act of detecting malicious attacks. Unauthorized users would never gain access to the system. The computer security is to limit the access to a computer system. An Intrusion detection system (IDS) is a software that monitors a single or a network of computers from malicious activities that steals the system information. IDS can distinguish between legitimate and illegitimate traffic and can able to signals attacks in real time, before malicious attacks occur. In this paper we describe a framework for building intrusion detection (ID) models. Machine learning algorithms are used for detecting attacks and helps the users to develop secure information systems.

Key Words

Machine learning, Intrusion detection system.

Cite This Article

"MACHINE LEARNING TECHNIQUES FOR INTRUSION DETECTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.59-64, June 2019, Available :http://www.jetir.org/papers/JETIR1908B09.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"MACHINE LEARNING TECHNIQUES FOR INTRUSION DETECTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp59-64, June 2019, Available at : http://www.jetir.org/papers/JETIR1908B09.pdf

Publication Details

Published Paper ID: JETIR1908B09
Registration ID: 227077
Published In: Volume 6 | Issue 6 | Year June-2019
DOI (Digital Object Identifier):
Page No: 59-64
Country: -, -, - .
Area: Engineering
ISSN Number: 2349-5162


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