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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Kertha Negara : Journal Ilmu Hukum Jurnal Sains dan Teknologi INFORMAL: Informatics Journal Aktual Justice Bioculture Journal KOMET
I Gede Adnyana
Fakultas Ekonomi dan Bisnis Universitas Udayana
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Law, Crime, Criminology & Criminal Justice Public Health Social Sciences

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Journal : IJCCS (Indonesian Journal of Computing and Cybernetics Systems)

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Hyperparameter Optimization Techniques for CNN-Based Cyber Security Attack Classification Adnyana, I Gede; Sugiartawan, Putu; Hartawan, I Nyoman Buda
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 3 (2024): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.98427

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.
Co-Authors Andika, I Gede Ariana, Anak Agung Gede Bagus Gandika Supartha, I Kadek Dwi I Kadek Dwi Gandika Supartha I Nyoman Buda Hartawan I WAYAN SUDIARSA Kadek Suryati Laksmi Dewi, Cokorda Istri Dian Made Gde Subha Karma Resen Merta, I Kadek Priyana Adi PUTU SUGIARTAWAN Socatama, I Putu Yoga Sumantra, I.K