Intrusion Detection Systems Using Hybrid Machine Learning Techniques

DOI

10.2991/978-94-6463-858-5_250How to use a DOI?

Keywords

Network Security; Intrusion Detection System (IDS); Machine Learning; Hybrid Model; Cybersecurity

Abstract

As our reliance on networked systems continues to grow, so does the risk of cybersecurity threats, which are becoming increasingly sophisticated and difficult to detect. Traditional Intrusion Detection Systems (IDS) often fall short in keeping up with these evolving threats, as they typically rely on predefined patterns that fail to spot new or unknown attacks. This paper introduces a hybrid machine learning-based Network Intrusion Detection System (NIDS), designed to enhance detection accuracy while reducing the number of false alarms—something that has long been a challenge for security systems. By combining both supervised and unsupervised learning techniques, the system can efficiently detect known attacks using models like Support Vector Machines (SVM), Decision Trees, Random Forest, and Neural Networks, while also identifying novel threats that haven’t been seen before through unsupervised methods. The research also incorporates a hybrid ensemble approach, which blends multiple models to improve overall accuracy and robustness. Experimental results on benchmark datasets demonstrate that the hybrid model significantly outperforms individual models in terms of precision, recall, and F1-score, offering a more reliable, adaptable, and efficient solution for real-time network security. This approach provides a promising path forward for building smarter, more effective intrusion detection systems. The hybrid model also shows great potential for deployment in real-world environments, where adaptability and accuracy are crucial. Ultimately, this research paves the way for future advancements in intrusion detection, helping to safeguard networks against an ever-growing range of cyber threats.

Copyright

© 2025 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  - P. Naveena
AU  - S. Varshitha
AU  - T. Hemanth
AU  - G. Jeswanth
AU  - B. Rasagnya
PY  - 2025
DA  - 2025/11/04
TI  - Intrusion Detection Systems Using Hybrid Machine Learning Techniques
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 2983
EP  - 2994
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_250
DO  - 10.2991/978-94-6463-858-5_250
ID  - Naveena2025
ER  -