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All Journal IT JOURNAL RESEARCH AND DEVELOPMENT INTECOMS: Journal of Information Technology and Computer Science JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Teknik dan Informatika CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Brahmana : Jurnal Penerapan Kecerdasan Buatan JPM: JURNAL PENGABDIAN MASYARAKAT Jurnal UNITEK Jurnal Nasional Teknologi Komputer International Conference on Sciences Development and Technology Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat International Journal of Economic, Technology and Social Sciences (Injects) AQILA : Acceleration, Quantum, Information Technology and Algorithm Journal
Sulistianingsih, Indri
Universitas Pembangunan Panca Budi
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Cyber Attack Prediction Using Machine Learning: A Comparative Study of Bayesian Network and Support Vector Machine Try Utari, Cut; Sulistianingsih, Indri; Diva Rofsyahfitri; Nurul Rizkina Kalsum Batubara; Wizdanil Yumna Nawar
Acceleration, Quantum, Information Technology and Algorithm Journal Vol. 2 No. 2 (2025): VOLUME 2, NO 2: DECEMBER 2025
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/aqila.v2i2.123

Abstract

Cybersecurity is becoming a critical issue with the increasing reliance on digital systems that are vulnerable to attacks. Proactive cyberattack prediction is one of the main approaches in early detection systems, where machine learning plays a strategic role. This research compares two popular machine learning algorithms, namely Bayesian Network and Support Vector Machine (SVM), to determine the most effective algorithm in predicting cyberattacks. This research uses two benchmark datasets, namely UNSW-NB15 and KDD99, as well as real attack data from Elazığ, Turkey. The analysis shows that the Bayesian Network implemented through the MCVAE_PBNN approach achieves up to 96% accuracy on the UNSW-NB15 dataset, with the advantage of detecting distributed and uncertain attacks. On the other hand, the SVM linear (SVML) algorithm showed a prediction accuracy of 95.02% in attack method classification, excelling in the case of data with clearly defined features. This study also analyzes the advantages and limitations of both algorithms, and provides implementation recommendations based on the needs of the detection system. The findings reinforce the urgency of developing adaptive predictive models in modern cybersecurity.
Co-Authors A.A. Ketut Agung Cahyawan W Ade Rizka afrisawati, Afrisawati Ahmad Akbar Ahmad Akbar Arie Candra Panjaitan Astri Mutia Rahma Astri Mutiara Rahma Bagus Kurniawan Darmeli Nasution Dito Putro Utomo Diva Rofsyahfitri Donny Sanjaya DWI SURYANTO Eko Hariyanto Elida Pane Fariz Harindra Syam Faza, Sharfina Fernando, Ahmad Fristi Riandari Ginting, Ramadhanu Heri Kurniawan Hermansyah Muhammad Arya Winatra Nova Maya Sari Nova Mayasari Nurul Rizkina Kalsum Batubara Oki Cindi Veronika Lumbanbatu Pane, Elida Putri, Nadya Andhika Rama Prameswara Ritonga Rian Farta Wijaya Rio Septian Hardinata Ririn Darma Putri Rizka, Ade Sari, Sri Novida Shindy Ramadhani Sri Novida Sari Sri Wahyuni SUheri Suherman Suherman Tasril, Virdyra Try Utari, Cut Virdyra Tasril Wirda Fitriani Wizdanil Yumna Nawar