Melola Discovery

← Melola Discovery

Jurnal SINTA 1

Important Features of CICIDS-2017 Dataset For Anomaly Detection in High Dimension and Imbalanced Class Dataset

Indonesian Journal of Electical Engineering and Informatics (IJEEI) · Vol. 9 No. 2 (2021) · hlm. 498–511

DOI
10.52549/ijeei.v9i2.3028
ISSN
2089-3272, 2089-3272
Terbit
2021-05-31
Penerbit
IAES Indonesian Section
Bahasa
en

Abstrak

The growth in internet traffic volume presents a new issue in anomaly detection, one of which is the high data dimension. The feature selection technique has been proven to be able to solve the problem of high data dimension by producing relevant features. On the other hand, high-class imbalance is a problem in feature selection. In this study, two feature selection approaches are proposed that are able to produce the most ideal features in the high-class imbalanced dataset. CICIDS-2017 is a reliable dataset that has a problem in high-class imbalance, therefore it is used in this study. Furthermore, this study performs experiments in Information Gain feature selection technique on the imbalance class datasaet. For validation, the Random Forest classification algorithm is used, because of its ability to handle multi-class data. The experimental results show that the proposed approaches have a very surprising performance, and surpass the state-of-the-art methods.

Kata kunci

Jaringan kolaborasi para penulis

40 penulis · 113 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.

  • Sriwijaya University
  • Universitas Dinamika Bangsa
  • Al Baha University
  • Udayana University
  • Universitas Ahmad Dahlan
  • Universiti Utara Malaysia
  • Universiti Malaysia Pahang Al-Sultan Abdullah
  • 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
Sharipuddin SharipuddinUniversitas Dinamika Bangsa18
Benni PurnamaUniversitas Dinamika Bangsa16
Darmawijoyo HanapiSriwijaya University12
Eko Arip WinantoUniversitas Dinamika Bangsa11
Nurul AfifahSriwijaya University7
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
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
Lola Yorita AstriUniversitas Dinamika Bangsa1
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
Ismat A. AldmourAl Baha University1
Thair Al-Dala’inAl Baha University1
Yusuf HartonoSriwijaya 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">Important Features of CICIDS-2017 Dataset For Anomaly Detection in High Dimension and Imbalanced Class Dataset</dc:title>
	<dc:creator xml:lang="en">Kurniabudi, Kurniabudi</dc:creator>
	<dc:creator xml:lang="en">Stiawan, Deris</dc:creator>
	<dc:creator xml:lang="en">Darmawijoyo, Darmawijoyo</dc:creator>
	<dc:creator xml:lang="en">Bin Idris, Mohd Yazid</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">feature selection</dc:subject>
	<dc:subject xml:lang="en">information gain</dc:subject>
	<dc:subject xml:lang="en">random forest</dc:subject>
	<dc:subject xml:lang="en">high class imbalance</dc:subject>
	<dc:subject xml:lang="en">CICIDS-2017</dc:subject>
	<dc:description xml:lang="en">The growth in internet traffic volume presents a new issue in anomaly detection, one of which is the high data dimension. The feature selection technique has been proven to be able to solve the problem of high data dimension by producing relevant features. On the other hand, high-class imbalance is a problem in feature selection. In this study, two feature selection approaches are proposed that are able to produce the most ideal features in the high-class imbalanced dataset. CICIDS-2017 is a reliable dataset that has a problem in high-class imbalance, therefore it is used in this study. Furthermore, this study performs experiments in Information Gain feature selection technique on the imbalance class datasaet. For validation, the Random Forest classification algorithm is used, because of its ability to handle multi-class data. The experimental results show that the proposed approaches have a very surprising performance, and surpass the state-of-the-art methods.</dc:description>
	<dc:publisher xml:lang="en">IAES Indonesian Section</dc:publisher>
	<dc:date>2021-05-31</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/3028</dc:identifier>
	<dc:identifier>10.52549/ijeei.v9i2.3028</dc:identifier>
	<dc:source xml:lang="en">Indonesian Journal of Electrical Engineering and Informatics (IJEEI); Vol. 9 No. 2: June 2021; 498-511</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/3028/627</dc:relation>
</oai_dc:dc>