Hyperparameter Optimization Techniques for CNN-Based Cyber Security Attack Classification

Adnyana, I Gede; Sugiartawan, Putu; Hartawan, I Nyoman Buda

doi:10.22146/ijccs.98427

Open access Jurnal SINTA 2

Hyperparameter Optimization Techniques for CNN-Based Cyber Security Attack Classification

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 18 No. 3 (2024)

DOI: 10.22146/ijccs.98427
ISSN: 2460-7258, 1978-1520
Published: 2024-07-31
Publisher: IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language: eng
License: creativecommons.org/licenses/by-sa/4.0

Abstract

Abstract The proliferation of cyber security attacks necessitates advanced and efficient detection methods. This study explores the application of Convolutional Neural Networks (CNNs) for classifying cyber security attacks using a comprehensive dataset containing various attack types and network traffic features. Emphasizing the role of hyperparameter optimization (HPO) techniques, this research aims to enhance the CNN model's performance in accurately detecting and classifying cyber attacks. Traditional machine learning approaches often need to catch up in capturing the complex patterns in such data, whereas CNNs excel in automatically extracting hierarchical features. Using the provided dataset, which includes attributes such as packet length, source and destination ports, protocol, and traffic type, we implemented various (HPO) techniques, including Grid Search, Random Search, and Bayesian Optimization, to identify the optimal CNN configurations. Our optimized CNN model significantly improved classification result. to baseline models without hyperparameter tuning. The results underline the importance of HPO in developing robust CNN models for cybersecurity applications. This study provides a practical framework for leveraging deep learning and optimization techniques to enhance cyber defense mechanisms, paving the way for future advancements in the field.

Keywords

Collaboration network of the authors

11 authors · 23 co-authorship links · 7 across different organisations

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Institut Bisnis dan Teknologi Indonesia
Okayama University
Politeknik Negeri Malang
Affiliation unknown

Different organisations
Same organisation
Affiliation unknown
Thicker line = more joint articles · bigger circle = more articles

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Co-author	Organisation	Joint articles
Ni Wayan Wardani	Okayama University	1
Anak Agung Surya Pradhana	Institut Bisnis dan Teknologi Indonesia	1
Kadek Suarjuna Batubulan	Politeknik Negeri Malang	1
I Nyoman Darma Kotama	Institut Bisnis dan Teknologi Indonesia	1
Kadek Dwi Novita Muliawati	Affiliation unknown	1
Ida Bagus Ary Indra Iswara	Affiliation unknown	1
Luh Gede Bevi Libraeni	Affiliation unknown	1
Komang Redy Winatha	Affiliation unknown	1

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://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.

Original metadata (oai_dc)

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	<dc:title xml:lang="en-US">Hyperparameter Optimization Techniques for CNN-Based Cyber Security Attack Classification</dc:title>
	<dc:creator>Adnyana, I Gede</dc:creator>
	<dc:creator>Sugiartawan, Putu</dc:creator>
	<dc:creator>Hartawan, I Nyoman Buda</dc:creator>
	<dc:subject xml:lang="en-US">Cyber Security</dc:subject>
	<dc:subject xml:lang="en-US">Cyber Security; Convolutional Neural Networks; Hyperparameter Optimization;atack Classification; Machine Learning; Network Traffic Data</dc:subject>
	<dc:description xml:lang="en-US">Abstract The proliferation of cyber security attacks necessitates advanced and efficient detection methods. This study explores the application of Convolutional Neural Networks (CNNs) for classifying cyber security attacks using a comprehensive dataset containing various attack types and network traffic features. Emphasizing the role of hyperparameter optimization (HPO) techniques, this research aims to enhance the CNN model's performance in accurately detecting and classifying cyber attacks. Traditional machine learning approaches often need to catch up in capturing the complex patterns in such data, whereas CNNs excel in automatically extracting hierarchical features. Using the provided dataset, which includes attributes such as packet length, source and destination ports, protocol, and traffic type, we implemented various (HPO) techniques, including Grid Search, Random Search, and Bayesian Optimization, to identify the optimal CNN configurations. Our optimized CNN model significantly improved classification result. to baseline models without hyperparameter tuning. The results underline the importance of HPO in developing robust CNN models for cybersecurity applications. This study provides a practical framework for leveraging deep learning and optimization techniques to enhance cyber defense mechanisms, paving the way for future advancements in the field.</dc:description>
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	<dc:date>2024-07-31</dc:date>
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	<dc:identifier>10.22146/ijccs.98427</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 18, No 3 (2024): July</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
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	<dc:rights xml:lang="en-US">Copyright (c) 2024 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
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