Melola Discovery

← Melola Discovery

Open access Journal SINTA 1

Semi-supervised approach for detecting distributed denial of service in SD-honeypot network environment

IAES International Journal of Artificial Intelligence · Vol. 11 No. 3 (2022) · pp. 1094–1100

DOI
10.11591/ijai.v11.i3.pp1094-1100
ISSN
2252-8938, 2089-4872
Published
2022-09-01
Publisher
Institute of Advanced Engineering and Science
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Distributed Denial of Service (DDoS) attacks is the most common type of cyber-attack. Therefore, an appropriate mechanism is needed to overcome those problems. This paper proposed an integration method between the honeypot sensor and software defined network (SDN) (SD-honeypot network). In terms of the attack detection process, the honeypot server utilized the Semi-supervised learning method in the attack classification process by combining the Pseudo-labelling model (support vector machine (SVM) algorithm) and the subsequent classification with the Adaptive Boosting method. The dataset used in this paper is monitoring data taken by the Suricata sensor. The research experiment was conducted by examining several variables, namely the accuracy, precision, and recall pointed at 99%, 66%, and 66%, respectively. The central processing unit (CPU) usage during classification was relatively small, which was around 14%. The average time of flow rule mitigation installation was 40s. In addition, the packet/prediction loss occurred during the attack, which caused several packets in the attack not to be classified was pointed at 43%.

Keywords

Collaboration network of the authors

40 authors · 84 co-authorship links · 3 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Gadjah Mada
  • Telkom University
  • Universitas Halu Oleo
  • Institut Agama Islam Negeri Kendari
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Selo SulistyoUniversitas Gadjah Mada2
Widyawan WidyawanUniversitas Gadjah Mada2
Muhammad AbizarAffiliation unknown2
Muhammad Ferry Septian Ihzanor SyahputraAffiliation unknown2
Ahmad Rizky HabibullahAffiliation unknown2
Solikhin SolikhinAffiliation unknown1
Eko RiyantoAffiliation unknown1
Achmad RizalTelkom University1
Jondri JondriTelkom University1
Sugondo HadiyosoTelkom University1
Sumiman UduUniversitas Halu Oleo1
La NiampeAffiliation unknown1
Hasrul Azwar HasibuanAffiliation unknown1
Rusiadi RusiadiAffiliation unknown1
Nasib NasibAffiliation unknown1
Affendy Abu HassimAffiliation unknown1
Suca RusdianAffiliation unknown1
Noor Aimi Mohamad PuadAffiliation unknown1
Rahma NurjanahAffiliation unknown1
Adi BhaktiAffiliation unknown1
Abu SanmasAffiliation unknown1
Asep Hedi TurmudiAffiliation unknown1
Burhan BurhanInstitut Agama Islam Negeri Kendari1
Rakhmat RakhmatAffiliation unknown1
Mubaddilah Rafa’alAffiliation unknown1
Timo KartinenAffiliation unknown1
Agoes Moh. MoefadAffiliation unknown1
Iklima SholichatiAffiliation unknown1
Kadar NurjamanAffiliation unknown1
Ajam MustajamAffiliation unknown1
Yusniar LubisAffiliation unknown1
Yusuf AbadiAffiliation unknown1
Fauzan RinoAffiliation unknown1
Lila BismalaAffiliation unknown1
Rasmulia SembiringAffiliation 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://ijai.iaescore.com/index.php/IJAI/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (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-US">Semi-supervised approach for detecting distributed denial of  service in SD-honeypot network environment</dc:title>
	<dc:creator>Setiawan Sumadi, Fauzi Dwi</dc:creator>
	<dc:creator>Kusuma Aditya, Christian Sri</dc:creator>
	<dc:creator>Maulana, Ahmad Akbar</dc:creator>
	<dc:creator>Syaifuddin, Syaifuddin</dc:creator>
	<dc:creator>Suryani, Vera</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">cyber security; distributed denial of service; honeypot; software defined network; semi-supervised;</dc:subject>
	<dc:description xml:lang="en-US">Distributed Denial of Service (DDoS) attacks is the most common type of cyber-attack. Therefore, an appropriate mechanism is needed to overcome those problems. This paper proposed an integration method between the honeypot sensor and software defined network (SDN) (SD-honeypot network). In terms of the attack detection process, the honeypot server utilized the Semi-supervised learning method in the attack classification process by combining the Pseudo-labelling model (support vector machine (SVM) algorithm) and the subsequent classification with the Adaptive Boosting method. The dataset used in this paper is monitoring data taken by the Suricata sensor. The research experiment was conducted by examining several variables, namely the accuracy, precision, and recall pointed at 99%, 66%, and 66%, respectively. The central processing unit (CPU) usage during classification was relatively small, which was around 14%. The average time of flow rule mitigation installation was 40s. In addition, the packet/prediction loss occurred during the attack, which caused several packets in the attack not to be classified was pointed at 43%.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2022-09-01</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-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/21744</dc:identifier>
	<dc:identifier>10.11591/ijai.v11.i3.pp1094-1100</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 11, No 3: September 2022; 1094-1100</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v11.i3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/21744/13436</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>