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Optimizing Machine Learning for Network Intrusion Detection with Random Forests: Securing Networks Against Evolving Cyber Threats in Digital Environments
Conference proceeding   Peer reviewed

Optimizing Machine Learning for Network Intrusion Detection with Random Forests: Securing Networks Against Evolving Cyber Threats in Digital Environments

Abdullah Al Mamun, Md. Abu Yousuf, Md. Kamal Hossen, Md Reazul Hassan Rizvi, Afjal Hossan Sarower, Md Jobair Hossain Faruk, Abm Kamrul Islam Riad and Hossain Shahriar
2025 IEEE 7th International Conference on Sustainable Technologies For Industry 5.0 (STI)
International Conference on Sustainable Technologies For Industry 5.0 (STI), 7th (Dhaka, Bangladesh, 12/11/2025–12/12/2025)
02/05/2026
DOI: https://doi.org/10.1109/STI69347.2025.11367516

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Abstract

In today's digital landscape, preventing security breaches presents significant challenges due to the rapid increase in computer traffic, making intrusion detection a paramount concern for network and computer security. The growing frequency and complexity of attacks jeopardize accessibility, confidentiality, reliability, and integrity, making it vital to implement effective intrusion detection systems (IDS). Traditional rule-based systems often struggle to address diverse and emerging cyber threats, leading to the successful implementation of machine learning algorithms, particularly random forest methods, in this field. In contrast to static rule-based models, machine learning (ML) approaches can adapt over time by learning from new threat patterns. This adaptability is crucial for identifying zero-day vulnerabilities that consecutive systems may miss. By looking at various real and fake events and spotting patterns like network type, port number, source, and destination, the random forest algorithm has shown to be very good at finding both familiar and new attacks. Its impressive accuracy rate of99.78 %underscores its significance as a powerful and versatile tool for modern intrusion detection systems, essential for safeguarding computer networks against malicious activities and unauthorized access. Furthermore, its robustness against overfitting makes it particularly suitable for dynamic and heterogeneous network environments. As cyber threats evolve, the adaptability and scalability of random forest-based IDS frameworks continue to make them a preferred choice for both enterprise and industrial applications.

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