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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) · pp. 498–511

DOI
10.52549/ijeei.v9i2.3028
ISSN
2089-3272, 2089-3272
Published
2021-05-31
Publisher
IAES Indonesian Section
Language
en

Abstract

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.

Keywords

Collaboration network of the authors

40 authors · 119 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
Sharipuddin SharipuddinUniversitas Dinamika Bangsa15
Benni PurnamaAffiliation unknown13
Mohd. Yazid IdrisAffiliation unknown10
Eko Arip WinantoUniversitas Dinamika Bangsa9
Darmawijoyo HanapiAffiliation unknown9
Ahmad HeryantoAffiliation unknown7
Tole SutiknoUniversitas Ahmad Dahlan5
Nurul AfifahAffiliation unknown5
Bhakti Yudho SupraptoSriwijaya University4
M. Agus Syamsul ArifinAffiliation unknown4
Yusuf HartonoSriwijaya University3
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
Lola Yorita AstriUniversitas Dinamika Bangsa1
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
Sindi SindiAffiliation unknown1
Muhammad Ridho RahmanAffiliation unknown1
Nora NurhalitaAffiliation unknown1

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) 
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	<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>
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