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Eka Pandu Cynthia
UIN Sultan Syarif Kasim RIau
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Journal : INOVTEK Polbeng - Seri Informatika

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Application of Machine Learning for Classifying and Identifying Security Threats Using a Supervised Learning Algorithm Approach Arta, Yudhi; Mohamad Samuri, Suzani; Syafitri, Nesi; Hanafiah, Anggi; Oktaria, Wina; Maripati, Maripati; Pandu Cynthia, Eka
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/aqjdbj22

Abstract

The rapid growth of harmful web content has intensified the demand for intelligent systems capable of accurately classifying cyber threats based on URL patterns. This study evaluates two widely used supervised learning algorithms, Random Forest and Naïve Bayes, for probabilistic classification of multi-class URL datasets. A synthetic dataset comprising 547,775 URLs was designed to reflect realistic threat distribution: benign (65.74%), phishing (14.46%), defacement (14.81%), and malware (4.99%). Each sample included simple structural features such as URL length, number of dots, HTTPS usage, and keyword indicators. Both models were tested using identical stratified train-test splits with varying sample sizes, including focused experiments on 15,000 and 100,000 entries. Results revealed that both models achieved high recall and precision only for the benign class, while failing to detect minority classes. For Random Forest, precision and recall for benign URLs reached 1.00 but dropped to 0.00 for phishing, defacement, and malware in all test scenarios. Naïve Bayes exhibited similar shortcomings, highlighting the impact of class imbalance and limited feature expressiveness. This research concludes that while Random Forest and Naïve Bayes are computationally efficient, they are inadequate for detecting cyber threats without preprocessing techniques such as SMOTE, cost-sensitive learning, or feature enrichment. Future work will explore adaptive hybrid models with contextual features and deep learning frameworks to enhance multi-class detection in real-world cybersecurity scenarios.
Co-Authors Adi Mustofa Afriyanti, Liza Ahyani Junia Karlina Alwis Nazir Anggi Pranata Anwar Alfaruqi Sipayung Aprijon Ardiansyah Saputra Arifandy, M. Imam Baehaqi Batubara, Supina Budianita , Elvia Chinthia, Maulidania Mediawati Dicky Abimanyu Dina Septiawati Edi Ismanto Effendi, Noverta Eka, Muhammad Elin Haerani Elvia Budianita Fadhilah Syafria Febi Yanto Fikri, Mhd Ikhsanul Fitra Kurnia Fitri Insani Fitri Wulandari Fitriani Muttakin Fitriani Muttakin Gultom, Imeldawaty Gusti, Siska Kurnia Hammam Zaki Hanafiah, Anggi Harahap, Ramadhan Hasdi Radiles Iis Afrianty Inggih Permana Intan Eria Elfi Iwan Iskandar Iwan Iskandar Jannata, Nanda Januar Al Amien Jasril Jasril Jeki Dwi Arisandi Khairuniza, Nabila Lestari Handayani M Imam Arifandy M. Afdal M. Afdal M. Afif Rizky A. M. Imam Arifandy Mardiah Maripati, Maripati mohamad samuri, suzani Muhammad Affandes Muhammad Amin Muhammad Fikry Muhammad Hasanuddin, Muhammad Muhammad Irsyad Muhammad Khairy Dzaky Muhammad Ridha MUHAMMAD YUSUF Muhammad Zen, Muhammad Mulyati, Sabar Mushlihul Afif Nazaruddin Nazaruddin Nazir, Alwis Nazruddin Safaat Nazruddin Safaat H Novi Yanti Novi Yanti nursalisah, febi Octadino Hariyadi Okfalisa Okfalisa Oktaria, Wina Pizaini Pizaini Putra, Randi Rian Rahmad Al Rian Rahmat Al Hafiz Rahmawati Raihan Mahdy Reski Mai Candra Ritonga, Sinta Wahyuni Rizki, Cindy Atika Roni Setyawan Rusmin Saragih, Rusmin Sarbaini Sarbaini Sinaga, Ayu Puspita Sari Siti Ramadhani Sugandi, Hatami Karsa Sulistia Ningsih, Sulistia Surya Agustian Suwanto Sanjaya Syafitri, Nesi Syaifullah Syaifullah Yelfi Yelfi Yelvi Fitriani Yelvi Fitriani Yelvi Vitriani Yenggi Putra Dinata Yudhi Arta, Yudhi Yusra Yusra . Yusra Yusra Yusra Yusra Yusra, Yusra Zulham Zulham