Machine Learning-Based Intrusion Detection for Cloud Security

Abstract

Cloud computing provides scalable and cost-effective resources for modern applications, but its distributed nature introduces complex security challenges. Traditional intrusion detection systems (IDS) often fail to detect evolving and sophisticated attacks in cloud environments. This paper proposes a Machine Learning-Based Intrusion Detection System (IDS) using the Random Forest (RF) algorithm to enhance detection accuracy and minimize false alarms. The model is trained on standard network intrusion datasets and evaluated using key performance metrics such as accuracy, precision, recall, and F1-score. Experimental results demonstrate that the proposed model achieves 97.8% detection accuracy, outperforming existing SVM and KNN-based IDS methods. The proposed framework provides a scalable and intelligent security layer for cloud infrastructures.

Introduction

The rapid rise of cloud computing has revolutionized data storage and access but also introduced serious security vulnerabilities due to shared and virtualized infrastructures. Intrusion Detection Systems (IDS) are crucial for monitoring and identifying unauthorized activities within cloud environments. Traditional signature- and rule-based IDS methods are limited in detecting zero-day and evolving attacks, motivating the integration of Machine Learning (ML) for intelligent and adaptive intrusion detection.

This study proposes a Random Forest–based IDS model designed to accurately classify network traffic as normal or malicious. The system utilizes datasets such as NSL-KDD or CICIDS2017, applying preprocessing (data cleaning, normalization, encoding), feature selection, and training-validation splits (70:30). The Random Forest algorithm constructs multiple decision trees and aggregates their outputs via majority voting to enhance accuracy and robustness.

Experimental results using Python’s Scikit-learn show that the Random Forest model achieved 97.8% accuracy, outperforming SVM (91.2%) and KNN (93.4%) in all key metrics — precision, recall, and F1-score. The model also demonstrated lower false positives and better scalability for large cloud datasets.

Conclusion

This paper presented a Machine Learning-Based Intrusion Detection System for cloud environments using the Random Forest algorithm. The system achieved superior performance in identifying attacks with high accuracy and reduced false positives. The research demonstrates the capability of ML models to enhance cloud infrastructure security. In future work, the model can be extended using Deep Learning techniques such as Convolutional Neural Networks (CNN) or Long Short-Term Memory (LSTM) networks to further improve detection accuracy and automate feature extraction. The system can also be integrated with real-time cloud monitoring tools for dynamic threat analysis.

References

[1] Kumar, A., et al., “Hybrid Intrusion Detection System for Cloud Networks,” International Journal of Computer Applications, 2021. [2] Li, X., and Zhang, Y., “Deep Learning Approaches for Cloud IDS,” Springer Journal of Cloud Computing, 2022. [3] Patel, M., et al., “Comparative Study of ML Algorithms for Intrusion Detection,” Elsevier Expert Systems with Applications, 2023. [4] Ahmed, R., et al., “Ensemble-Based IDS in Cloud Environments,” IEEE Access, 2024. [5] NSL-KDD Dataset, Available at: http://www.unb.ca/cic/datasets/nsl.html

Copyright

Copyright © 2025 Mr. Divakar T K, Dr. Vinutha M R. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.