Detecting threats in internet domain data is critical for maintaining secure cyberspace and protecting users from cyber attacks. Traditional rule-based systems often fall short in handling the scale and evolving nature of such threats. Therefore, machine learning-based approaches have gained prominence due to their adaptability and pattern recognition capabilities. This research presents a comparative analysis of three widely used algorithms: Random Forest (RF), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM). The aim is to evaluate how effectively each model identifies malicious domains. The algorithms are assessed using performance metrics such as accuracy, precision, recall and F1-score. Our results indicate that while LSTM achieves the highest detection accuracy, it requires more computational resources and longer training time. On the other hand, Random Forest shows strong performance with faster execution, making it suitable for real-time applications. The Support Vector Machine performs reasonably well but is sensitive to feature scaling and may underperform on larger datasets. This comparative study provides valuable insights for researchers and security practitioners seeking effective solutions for automated domain threat detection.

Internet domains, threat detection, Random Forest, SVM, LSTM, cybersecurity, DNS monitoring, domain classification, machine learning, phishing detection.

Giovanni A., Mauro A., Michele C., Mirco M. (2019). Hardening Random Forest Cyber Detectors Against Adversarial Attacks. IEEE Transactions on Emerging Topics in Computational Intelligence, 427 - 439.

Archan Mitra (2024). Real-Time Threat Detection in Cybersecurity: Leveraging Machine Learning Algorithms for Enhanced Anomaly Detection. Machine Intelligence Applications in Cyber-Risk Management , 315-344.

Momen H., Mohamed E., Mohamed B., Ahmed M., Mohamed G., Mena Hany (2024). Evaluating Predictive Models in Cybersecurity: A Comparative Analysis of Machine and Deep Learning Techniques for Threat Detection.

Ansarullah Hasas, Mohammad Shuaib Zarinkhail, Musawer Hakimi, Mohammad Mustafa Quchi (2024). Strengthening Digital Security: Dynamic Attack Detection with LSTM, KNN, and Random Forest. Journal of Computer Science and Technology Studies, 49-57.

Prasenjit Dey, Dhananjoy Bhakta (2023). A New Random Forest and Support Vector Machine‑based Intrusion Detection Model in Networks. National Academy Science Letters, 46(5), 471-477.

Atheer Alaa Hammad(2024). Random Forest and LSTM Hybrid Model for Detecting DDoS Attacks in Healthcare IoT Networks. CyberSystem Journal, 1(2), 1-8.

Zhang, Q., & Li, X. (2022). Advancements in Online System Security: A Focus on User Activity Monitoring. Journal of Information Security, 18(1), 33-47.

Smith, J., & Brown, A. (2021). Cybersecurity and User Monitoring in Online Platforms. International Journal of Cyber Security, 15(3), 45-58.

Rai, K., Devi, M. S., & Guleria, A. (2016). Decision Tree Based Algorithm for Intrusion Detection. International Journal of Advanced Networking and Applications, 7, 2828–2834.

Szegedy, C., Toshev, A., & Erhan, D. (2013). Deep Neural Networks for Object Detection. Proceedings of the 26th International Conference on Neural Information Processing Systems—Volume 2, 2553–2561.

Dhanabal, L., & Shantharajah, S. P. (2015). A Study on NSL_KDD Dataset for Intrusion Detection System Based on Classification Algorithms. International Journal of Advanced Research in Computer and Communication Engineering, 4, 446–452.

Staudemeyer, R. C. (2015). Applying long short-term memory recurrent neural networks to intrusion detection. South African Computer Journal, 56, 136–154.