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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) · hlm. 1094–1100

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

Abstrak

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%.

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22 penulis · 46 tautan kepenulisan bersama · 11 lintas organisasi

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  • Universitas Muhammadiyah Malang
  • Institut Teknologi Sepuluh Nopember
  • Telkom University
  • Universitas Gadjah Mada
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Rekan penulisOrganisasiArtikel bersama
Selo SulistyoUniversitas Gadjah Mada2
Widyawan WidyawanUniversitas Gadjah Mada2
Muhammad AbizarUniversitas Muhammadiyah Malang2
Muhammad Ferry Septian Ihzanor SyahputraUniversitas Muhammadiyah Malang2
Ahmad Rizky HabibullahUniversitas Muhammadiyah Malang2
Wempy Aditya WiryawanAfiliasi tidak diketahui1
Agus Zainal ArifinInstitut Teknologi Sepuluh Nopember1
Mamluatul Hani`ahInstitut Teknologi Sepuluh Nopember1
Aryo HartoInstitut Teknologi Sepuluh Nopember1
Chastine FatichahInstitut Teknologi Sepuluh Nopember1
Diana PurwitasariInstitut Teknologi Sepuluh Nopember1
Achmad RizalTelkom University1
Jondri JondriTelkom University1
Sugondo HadiyosoTelkom University1
Yuda MunarkoUniversitas Muhammadiyah Malang1
Nur Putri HidayahUniversitas Muhammadiyah Malang1
Galih Wasis WicaksonoUniversitas Muhammadiyah Malang1

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Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<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>
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	<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>