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AN ENHANCED SQL INJECTION DETECTION USING ENSEMBLE METHOD

JUTI: Jurnal Ilmiah Teknologi Informasi · Vol. 21 No. 1 (2023) · pp. 1–9

DOI
10.12962/j24068535.v21i1.a1060
ISSN
2406-8535, 1412-6389
Published
2023-01-31
Publisher
Institut Teknologi Sepuluh Nopember
Language
eng

Abstract

SQL injection is a cybercrime that attacks websites. This issue is still a challenging issue in the realm of security that must be resolved. These attacks are very costly financially, which count millions of dollars each year. Due to large data leaks, the losses also impact the world economy, which averages nearly $50 per year, and most of them are caused by SQL injection. In a study of 300,000 attacks worldwide in any given month, 24.6% were SQL injection. Therefore, implementing a strategy to protect against web application attacks is essential and not easy because we have to protect user privacy and enterprise data. This study proposes an enhanced SQL injection detection using the voting classifier method based on several machine learning algorithms. The proposed classifier could achieve the highest accuracy from this research in 97.07%.

Collaboration network of the authors

34 authors · 94 co-authorship links

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  • Institut Teknologi Sepuluh Nopember
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Supeno DjanaliInstitut Teknologi Sepuluh Nopember3
Wahyu SuadiInstitut Teknologi Sepuluh Nopember2
Ilham Gurat AdillionInstitut 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
Prima WiratamaInstitut Teknologi Sepuluh Nopember1
Hazna At ThooriqohInstitut Teknologi Sepuluh Nopember1
M. Naufal AzzmiInstitut Teknologi Sepuluh Nopember1
Yoga Ari TofanInstitut Teknologi Sepuluh Nopember1
Rully SoelaimanInstitut Teknologi Sepuluh Nopember1
Suhadi LiliInstitut Teknologi Sepuluh Nopember1
Muhammad Raihan AninditaInstitut Teknologi Sepuluh Nopember1
Umi Laili YuhanaInstitut 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
Izra Noor Zahara AliyaAffiliation 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.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">AN ENHANCED SQL INJECTION DETECTION USING ENSEMBLE METHOD</dc:title>
	<dc:creator xml:lang="en">Purbawa, Doni Putra</dc:creator>
	<dc:creator xml:lang="en">Ulhaq, Azzam Jihad</dc:creator>
	<dc:creator xml:lang="en">Ikhsan, Gusna</dc:creator>
	<dc:creator xml:lang="en">Shiddiqi, Ary Mazharuddin</dc:creator>
	<dc:description xml:lang="en">SQL injection is a cybercrime that attacks websites. This issue is still a challenging issue in the realm of security that must be resolved. These attacks are very costly financially, which count millions of dollars each year. Due to large data leaks, the losses also impact the world economy, which averages nearly $50 per year, and most of them are caused by SQL injection. In a study of 300,000 attacks worldwide in any given month, 24.6% were SQL injection. Therefore, implementing a strategy to protect against web application attacks is essential and not easy because we have to protect user privacy and enterprise data. This study proposes an enhanced SQL injection detection using the voting classifier method based on several machine learning algorithms. The proposed classifier could achieve the highest accuracy from this research in 97.07%.</dc:description>
	<dc:publisher xml:lang="en">Institut Teknologi Sepuluh Nopember</dc:publisher>
	<dc:date>2023-01-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://juti.if.its.ac.id/index.php/juti/article/view/1060</dc:identifier>
	<dc:identifier>10.12962/j24068535.v21i1.a1060</dc:identifier>
	<dc:source xml:lang="en">JUTI: Jurnal Ilmiah Teknologi Informasi; Vol. 21, No. 1, January 2023; 1-9</dc:source>
	<dc:source>2406-8535</dc:source>
	<dc:source>1412-6389</dc:source>
	<dc:source>10.12962/j24068535.v21i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://juti.if.its.ac.id/index.php/juti/article/view/1060/488</dc:relation>
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