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All Journal Sistemasi: Jurnal Sistem Informasi CogITo Smart Journal IT JOURNAL RESEARCH AND DEVELOPMENT Bulletin of Computer Science Research Journal of Informatics Management and Information Technology Mejuajua The Indonesian Journal of Computer Science INOVTEK Polbeng - Seri Informatika
Yudhi Arta, Yudhi
Universitas Islam Riau
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Public Health Other

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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 Abdul Syukur Daulay, Suandi Fadhilla, Mutia Farradinna, Syarifah Hanafiah, Anggi Herawati, Icha Jayanti, Wella Kharisma, Roni Lestari, Yuyun Dwi Maripati, Maripati mohamad samuri, suzani Nasution, Hafiza Oktasia Novendra, Yoldi Nur, Nuriman M Oktaria, Wina Oktaviaani, Rizka Pandu Cynthia, Eka Rachmat Setiawan, Panji Rizky Wandri Setiawan, Panji Rachmat Syafitri, Nesi Syefriani