Enhancing Network Intrusion Detection with Random Forest and Federated Learning Algorithms

DOI

10.2991/978-94-6239-616-6_83How to use a DOI?

Keywords

Network intrusion detection; user authentication; Random Forest classification; monitoring; cybersecurity dashboard

Abstract

Network security threats are evolving rapidly, becoming more complex and harder to detect, which makes it essential for organizations to adopt intrusion detection systems (IDS) that not only provide accurate results but also ensure accountability in safeguarding critical infrastructures. Traditional IDS frameworks typically rely on anomaly or misuse detection, but they often lack integrated user authentication and monitoring features, leading to operational inefficiencies and gaps in tracking security incidents. To address these challenges, this work proposes a comprehensive IDS that combines a JSON-based user authentication mechanism with a Random Forest–based intrusion detection model, offering both secure access control and robust threat classification. Developed using Python, Scikit-learn, and Pandas for data processing, with Pickle for model persistence, the system is deployed on an interactive Streamlit dashboard for real-time monitoring and user-friendly operation. Capable of detecting multiple categories of attacks including DoS, Probe, R2L, and U2R, the IDS ensures that every action is tied to an authenticated user, thereby enhancing accountability in system usage. Experimental results demonstrate that integrating access management with machine learning–driven detection improves coordination, reduces delays in response, and strengthens organizational trust in security operations, highlighting the importance of merging authentication and intrusion detection to build a more efficient, transparent, and comprehensive cybersecurity framework.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - M. Madhumitha
AU  - M. Karthik
AU  - A. Dheeraj
AU  - S. Dashvant
PY  - 2026
DA  - 2026/03/31
TI  - Enhancing Network Intrusion Detection with Random Forest and Federated Learning Algorithms
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 1138
EP  - 1151
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_83
DO  - 10.2991/978-94-6239-616-6_83
ID  - Madhumitha2026
ER  -