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Jurnal SINTA 2

MALICIOUS TRAFFIC DETECTION IN DNS INFRASTRUCTURE USING DECISION TREE ALGORITHM

JUTI: Jurnal Ilmiah Teknologi Informasi · Vol. 20 No. 1 (2022) · pp. 45–52

DOI
10.12962/j24068535.v19i3.a1054
ISSN
2406-8535, 1412-6389
Published
2022-01-31
Publisher
Institut Teknologi Sepuluh Nopember
Language
eng

Abstract

Domain Name System (DNS) is an essential component in internet infrastructure to direct domains to IP addresses or conversely. Despite its important role in delivering internet services, attackers often use DNS as a bridge to breach a system. A DNS traffic analysis system is needed for early detection of attacks. However, the available security tools still have many shortcomings, for example broken authentication, sensitive data exposure, injection, etc. This research uses DNS analysis to develop anomaly-based techniques to detect malicious traffic on the DNS infrastructure. To do this, We look for network features that characterize DNS traffic. Features obtained will then be processed using the Decision Tree algorithm to classifyincoming DNS traffic. We experimented with 2.291.024 data traffic data matches the characteristics of BotNet and normal traffic. By dividing the data into 80% training and 20% testing data, our experimental results showed high detection aacuracy (96.36%) indicating the robustness of our method.

Collaboration network of the authors

40 authors · 114 co-authorship links · 5 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.

  • Institut Teknologi Sepuluh Nopember
  • Politeknik Elektronika Negeri Surabaya
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Supeno DjanaliInstitut Teknologi Sepuluh Nopember3
Wahyu SuadiInstitut Teknologi Sepuluh Nopember2
Ilham Gurat AdillionInstitut Teknologi Sepuluh Nopember2
Umi Laili YuhanaInstitut Teknologi Sepuluh Nopember2
Faosan MapaAffiliation unknown1
Rakhmadany PrimanandaAffiliation unknown1
Baskoro Adi PratomoInstitut Teknologi Sepuluh Nopember1
Junaidi JunaidiInstitut Teknologi Sepuluh Nopember1
Prasetyo Adi WibowoInstitut Teknologi Sepuluh Nopember1
Dini YuniasriInstitut Teknologi Sepuluh Nopember1
Putri DamayantiInstitut Teknologi Sepuluh Nopember1
Waskitho WibisonoInstitut Teknologi Sepuluh Nopember1
Radityo AnggoroInstitut Teknologi Sepuluh Nopember1
Jauari Akhmad Nur HasimPoliteknik Elektronika Negeri Surabaya1
Umi Sa’adahPoliteknik Elektronika Negeri Surabaya1
Andhik Ampuh YunantoPoliteknik Elektronika Negeri Surabaya1
Desy Intan PermatasariPoliteknik Elektronika Negeri Surabaya1
Fadilah Fahrul HardiansyahPoliteknik Elektronika Negeri Surabaya1
Irma Rofni WulandariAffiliation unknown1
Tiara Nur AnnisaInstitut Teknologi Sepuluh Nopember1
Prima WiratamaInstitut Teknologi Sepuluh Nopember1
Doni Putra PurbawaInstitut Teknologi Sepuluh Nopember1
Azzam Jihad UlhaqInstitut Teknologi Sepuluh Nopember1
Gusna IkhsanInstitut Teknologi Sepuluh Nopember1
Rully SoelaimanInstitut Teknologi Sepuluh Nopember1
Suhadi LiliInstitut Teknologi Sepuluh Nopember1
Muhammad Raihan AninditaInstitut Teknologi Sepuluh Nopember1
Muhammad Faisal FananiInstitut Teknologi Sepuluh Nopember1
Bilqis AmaliahInstitut Teknologi Sepuluh Nopember1
Bagaskoro Kuncoro ArdiInstitut Teknologi Sepuluh Nopember1
I Komang Ari MogiInstitut Teknologi Sepuluh Nopember1
Agung Mustika RizkiInstitut Teknologi Sepuluh Nopember1
Bintang NuralamsyahInstitut Teknologi Sepuluh Nopember1
Moch. Nafkhan AlzamzamiInstitut Teknologi Sepuluh Nopember1
Christian Budhi SabdanaAffiliation unknown1
Noriandini Dewi SalyasariAffiliation 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) https://juti.if.its.ac.id/index.php/juti/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
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	<dc:title xml:lang="en">MALICIOUS TRAFFIC DETECTION IN DNS INFRASTRUCTURE USING DECISION TREE ALGORITHM</dc:title>
	<dc:creator xml:lang="en">Thooriqoh, Hazna At</dc:creator>
	<dc:creator xml:lang="en">Azzmi, M. Naufal</dc:creator>
	<dc:creator xml:lang="en">Tofan, Yoga Ari</dc:creator>
	<dc:creator xml:lang="en">Shiddiqi, Ary Mazharuddin</dc:creator>
	<dc:description xml:lang="en">Domain Name System (DNS) is an essential component in internet infrastructure to direct domains to IP addresses or conversely. Despite its important role in delivering internet services, attackers often use DNS as a bridge to breach a system. A DNS traffic analysis system is needed for early detection of attacks. However, the available security tools still have many shortcomings, for example broken authentication, sensitive data exposure, injection, etc. This research uses DNS analysis to develop anomaly-based techniques to detect malicious traffic on the DNS infrastructure. To do this, We look for network features that characterize DNS traffic. Features obtained will then be processed using the Decision Tree algorithm to classifyincoming DNS traffic. We experimented with 2.291.024 data traffic data matches the characteristics of BotNet and normal traffic. By dividing the data into 80% training and 20% testing data, our experimental results showed high detection aacuracy (96.36%) indicating the robustness of our method.</dc:description>
	<dc:publisher xml:lang="en">Institut Teknologi Sepuluh Nopember</dc:publisher>
	<dc:date>2022-01-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://juti.if.its.ac.id/index.php/juti/article/view/1054</dc:identifier>
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	<dc:source xml:lang="en">JUTI: Jurnal Ilmiah Teknologi Informasi; Vol. 20, No. 1, January 2022; 45-52</dc:source>
	<dc:source>2406-8535</dc:source>
	<dc:source>1412-6389</dc:source>
	<dc:source>10.12962/j24068535.v19i3</dc:source>
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