Melola Discovery

← Melola Discovery

Journal SINTA 1

Enhanced Deep Learning Intrusion Detection in IoT Heterogeneous Network with Feature Extraction

Indonesian Journal of Electical Engineering and Informatics (IJEEI) · Vol. 9 No. 3 (2021) · pp. 747–755

DOI
10.52549/.v9i3.3134
ISSN
2089-3272, 2089-3272
Published
2021-09-20
Publisher
IAES Indonesian Section
Language
en

Abstract

Heterogeneous network is one of the challenges that must be overcome in Internet of Thing Intrusion Detection System (IoT IDS). The difficulty of the IDS significantly is caused by various devices, protocols, and services, that make the network becomes complex and difficult to monitor. Deep learning is one algorithm for classifying data with high accuracy. This research work incorporated Deep Learning into IDS for IoT heterogeneous networks. There are two concerns on IDS with deep learning in heterogeneous IoT networks, i.e.: limited resources and excessive training time. Thus, this paper uses Principle Component Analysis (PCA) as features extraction method to deal with data dimensions so that resource usage and training time will be significantly reduced. The results of the evaluation show that PCA was successful reducing resource usage with less training time of the proposed IDS with deep learning in heterogeneous networks environment. Experiment results show the proposed IDS achieve overall accuracy above 99%.

Keywords

Collaboration network of the authors

40 authors · 126 co-authorship links · 25 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Dinamika Bangsa
  • Sriwijaya University
  • Universitas Ahmad Dahlan
  • Universiti Utara Malaysia
  • Al Baha University
  • Diponegoro University
  • Sultan Agung Islamic University
  • Other organisations
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Mohd. Yazid IdrisAffiliation unknown17
Darmawijoyo DarmawijoyoAffiliation unknown9
Nurul AfifahAffiliation unknown8
Ahmad HeryantoAffiliation unknown7
Mohd Yazid Bin IdrisAffiliation unknown6
Tole SutiknoUniversitas Ahmad Dahlan5
Bhakti Yudho SupraptoSriwijaya University4
M. Agus Syamsul ArifinAffiliation unknown4
Lola Yorita AstriUniversitas Dinamika Bangsa3
Munawar Agus RiyadiDiponegoro University2
Lina HandayaniUniversitas Ahmad Dahlan2
Imam Much Ibnu SubrotoSultan Agung Islamic University2
Moh’D Suliman ShakkahAffiliation unknown2
Khaled AlaqeelAffiliation unknown2
Ali AlfageehAffiliation unknown2
Setiawan AssegaffAffiliation unknown2
Hendri HendriUniversitas PTIQ Jakarta2
Ahmad ZarkasiAffiliation unknown2
Emma Trinurani SofyanAffiliation unknown2
Stefina Liana SariAffiliation unknown2
Saefur RohmanAffiliation unknown2
Yudi NoviantoUniversitas Dinamika Bangsa2
Ibnu Sani WijayaUniversitas Dinamika Bangsa2
Pareza Alam JusiaUniversitas Dinamika Bangsa2
Khalil Ibrahim GhathwanUniversiti Utara Malaysia1
Abdul Razak YaakubUniversiti Utara Malaysia1
Setiawan AssegafAffiliation unknown1
Yanif Dwi KuntjoroIndonesia Defense University1
I Gede YusaUdayana University1
Dewi BungaAffiliation unknown1
Mohd Faaizie DarmawanUniversiti Malaysia Pahang Al-Sultan Abdullah1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) http://section.iaesonline.com/index.php/IJEEI/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en">Enhanced Deep Learning Intrusion Detection in IoT Heterogeneous Network with Feature Extraction</dc:title>
	<dc:creator xml:lang="en">Sharipuddin, Sharipuddin</dc:creator>
	<dc:creator xml:lang="en">Winanto, Eko Arip</dc:creator>
	<dc:creator xml:lang="en">Purnama, Benni</dc:creator>
	<dc:creator xml:lang="en">Kurniabudi, Kurniabudi</dc:creator>
	<dc:creator xml:lang="en">Stiawan, Deris</dc:creator>
	<dc:creator xml:lang="en">Hanapi, Darmawijoyo</dc:creator>
	<dc:creator xml:lang="en">Idris, Mohd Yazid bin</dc:creator>
	<dc:creator xml:lang="en">Kerim, Bedine</dc:creator>
	<dc:creator xml:lang="en">Budiarto, Rahmat</dc:creator>
	<dc:subject xml:lang="en">IDS Features Extraction  PCA Deep Learning Heterogeneous IoT</dc:subject>
	<dc:description xml:lang="en">Heterogeneous network is one of the challenges that must be overcome in Internet of Thing Intrusion Detection System (IoT IDS). The difficulty of the IDS significantly is caused by various devices, protocols, and services, that make the network becomes complex and difficult to monitor. Deep learning is one algorithm for classifying data with high accuracy. This research work incorporated Deep Learning into IDS for IoT heterogeneous networks. There are two concerns on IDS with deep learning in heterogeneous IoT networks, i.e.: limited resources and excessive training time. Thus, this paper uses Principle Component Analysis (PCA) as features extraction method to deal with data dimensions so that resource usage and training time will be significantly reduced. The results of the evaluation show that PCA was successful reducing resource usage with less training time of the proposed IDS with deep learning in heterogeneous networks environment. Experiment results show the proposed IDS achieve overall accuracy above 99%.</dc:description>
	<dc:publisher xml:lang="en">IAES Indonesian Section</dc:publisher>
	<dc:date>2021-09-20</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://section.iaesonline.com/index.php/IJEEI/article/view/3134</dc:identifier>
	<dc:identifier>10.52549/.v9i3.3134</dc:identifier>
	<dc:source xml:lang="en">Indonesian Journal of Electrical Engineering and Informatics (IJEEI); Vol. 9 No. 3: September 2021; 747-755</dc:source>
	<dc:source>2089-3272</dc:source>
	<dc:source>2089-3272</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://section.iaesonline.com/index.php/IJEEI/article/view/3134/643</dc:relation>
</oai_dc:dc>