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Intrusion Detection Based on Deep Learning

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Last updated: 25 Dec 2024

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Abstract

Information and Communication Technology (ICT) plays an important role in our life. ICT is engaged with the business and individual patterns of human life. The ICT security is one of the normal ICT fields, which attracts researchers' attention. The objective of security is to discover attacks represented in control and data planes. These attacks include Denial of Service (DoS), and probing attacks. Intrusion Detection System (IDS) is one of the best solutions for observing, and distinguishing these attacks. In this paper, an IDS dependent on Deep Learning (DL) is proposed. This system achieves an accuracy detection level of 100%.

DOI

10.21608/mjeer.2019.76787

Keywords

Intrusion Detection System (IDS), Deep Learning (DL) and Convolutional Neural Network (CNN)

Authors

First Name

Youssef F.

Last Name

Sallam

MiddleName

-

Affiliation

Communications and Electronics Department Faculty of Electronic Engineering,Menoufia University: Menouf, Egypt

Email

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Orcid

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First Name

Ahmed

Last Name

Sedik

MiddleName

-

Affiliation

The Robotics and Intelligent Machines Department Faculty of Aritificial Intelligence KafrElsheikh University: Kafr ElSheikh, Egypt

Email

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City

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Orcid

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First Name

Rania

Last Name

Ghazy

MiddleName

-

Affiliation

Communications and Electronics Department Faculty of Electronic Engineering,Menoufia University: Menouf, Egypt

Email

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City

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Orcid

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First Name

Nirmeen

Last Name

Abdelwahab

MiddleName

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Affiliation

Engineering and Computer Science Department Faculty of Electronic Engineering,Menoufia University: Menouf, Egypt

Email

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Orcid

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First Name

HossamEl-din H.

Last Name

Ahmed

MiddleName

-

Affiliation

Communications and Electronics Department Faculty of Electronic Engineering,Menoufia University: Menouf, Egypt

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City

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Orcid

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First Name

Adel

Last Name

Saleeb

MiddleName

-

Affiliation

Communications and Electronics Department Faculty of Electronic Engineering,Menoufia University: Menouf, Egypt

Email

aasaleeb@hotmail.com

City

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Orcid

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First Name

Ghada M.

Last Name

El Banby

MiddleName

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Affiliation

Industrial Electronics and Control Department Faculty of Electronic Engineering,Menoufia University: Menouf, Egypt

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City

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Orcid

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First Name

Ashraf A. M.

Last Name

Khalaf

MiddleName

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Affiliation

Electronics and Communications Department Faculty of Engineering Minia University: Minia, Egypt

Email

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City

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Orcid

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First Name

Fathi E.

Last Name

Abd El-Samie

MiddleName

-

Affiliation

Communications and Electronics Department Faculty of Electronic Engineering, Menoufia University: Menouf, Egypt

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City

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Orcid

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Volume

28

Article Issue

ICEEM2019-Special Issue

Related Issue

9704

Issue Date

2019-12-01

Receive Date

2020-03-10

Publish Date

2019-12-01

Page Start

369

Page End

373

Print ISSN

1687-1189

Online ISSN

2682-3535

Link

https://mjeer.journals.ekb.eg/article_76787.html

Detail API

https://mjeer.journals.ekb.eg/service?article_code=76787

Order

43

Type

Original Article

Type Code

1,088

Publication Type

Journal

Publication Title

Menoufia Journal of Electronic Engineering Research

Publication Link

https://mjeer.journals.ekb.eg/

MainTitle

Intrusion Detection Based on Deep Learning

Details

Type

Article

Created At

22 Jan 2023