Melola Discovery

← Melola Discovery

Jurnal 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) · hlm. 747–755

DOI
10.52549/.v9i3.3134
ISSN
2089-3272, 2089-3272
Terbit
2021-09-20
Penerbit
IAES Indonesian Section
Bahasa
en

Abstrak

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%.

Kata kunci

Jaringan kolaborasi para penulis

40 penulis · 118 tautan kepenulisan bersama · 79 lintas organisasi · menampilkan 40 yang paling banyak terhubung

Arahkan kursor atau ketuk sebuah nama untuk menyorot rekan penulisnya. Klik dua kali untuk membuka halaman penulis.

  • Universitas Dinamika Bangsa
  • Sriwijaya University
  • Udayana University
  • Universitas Ahmad Dahlan
  • Universiti Utara Malaysia
  • Universiti Malaysia Pahang Al-Sultan Abdullah
  • University of Technology Malaysia
  • Organisasi lain
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Nurul AfifahSriwijaya University10
Darmawijoyo DarmawijoyoSriwijaya University10
M. Agus Syamsul ArifinUniversitas Bina Insan6
Tole SutiknoUniversitas Ahmad Dahlan5
Munawar Agus RiyadiDiponegoro University4
Bhakti Yudho SupraptoSriwijaya University4
Yaya Sudarya TrianaMercu Buana University4
Mohd Azam OsmanUniversiti Sains Malaysia4
Lola Yorita AstriUniversitas Dinamika Bangsa3
Lina HandayaniUniversitas Ahmad Dahlan2
Imam Much Ibnu SubrotoSultan Agung Islamic University2
Moh’D Suliman ShakkahAfiliasi tidak diketahui2
Khaled AlaqeelAfiliasi tidak diketahui2
Ali AlfageehAfiliasi tidak diketahui2
Setiawan AssegaffUniversitas Dinamika Bangsa2
Hendri HendriUniversitas Islam Negeri Ar-Raniry2
Shahreen binti KasimTun Hussein Onn University of Malaysia2
Ahmad ZarkasiSriwijaya University2
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 AssegafAfiliasi tidak diketahui1
Yanif Dwi KuntjoroIndonesia Defense University1
I Gede YusaUdayana University1
Dewi BungaUniversitas Hindu Negeri I Gusti Bagus Sugriwa Denpasar1
Mohd Faaizie DarmawanUniversiti Malaysia Pahang Al-Sultan Abdullah1
Ahmad Firdaus Zainal AbidinUniversiti Malaysia Pahang Al-Sultan Abdullah1
Ni Ketut Supasti DharmawanUdayana University1
Desak Putu Dewi KasihUdayana University1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://section.iaesonline.com/index.php/IJEEI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (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>