A Survey of Intrusion Detection Systems Based on Machine Learning for Cloud Security

International Journal of Electrical and Electronics Engineering

© 2025 by SSRG - IJEEE Journal

Volume 12 Issue 5

Year of Publication : 2025

Authors : Khatha Mahendar, Gandla Shivakanth

10.14445/23488379/IJEEE-V12I5P119

How to Cite?

Khatha Mahendar, Gandla Shivakanth, "A Survey of Intrusion Detection Systems Based on Machine Learning for Cloud Security," SSRG International Journal of Electrical and Electronics Engineering, vol. 12,  no. 5, pp. 226-242, 2025. Crossref, https://doi.org/10.14445/23488379/IJEEE-V12I5P119

Abstract:

The fast growth of cloud computing has resulted in greater dependence on scalable and agile infrastructure. Nevertheless, this change has also brought about serious cybersecurity issues, especially intrusion detection. Conventional Intrusion Detection Systems (IDS) are challenged by detecting new attacks, dealing with massive cloud environments, and keeping up with real-time threat detection. Machine Learning (ML) has shown great potential to improve IDS functionality by automating anomaly detection, enhancing accuracy, and adjusting to changing threats. This survey presents a complete overview of ML-based IDS for cloud security, emphasizing supervised, unsupervised, and deep learning methods. It investigates the benefits and weaknesses of current methods, emphasizing their detection performance, scalability, and computation load. Moreover, this study examines widely utilized datasets, touches upon adversarial attacks and privacy issues, and explores upcoming trends such as Explainable AI, Zero Trust Architecture, and adaptive IDS models. By filling the gap between research and real-world implementation, this survey seeks to inform future developments in cloud environment security against advanced cyber threats.

Keywords:

Cloud computing security, Intrusion Detection Systems (IDS), Anomaly Detection Systems (ADS), Machine learning algorithms, Network traffic analysis.

References:

[1] Caitlin Harris, 50 Cloud Security Stats You Should Know In 2025, Expert Insights, 2025. [Online]. Available: https://expertinsights.com/insights/50-cloud-security-stats-you-should-know/
[2] Grace Lau, 40+ Alarming Cloud Security Statistics for 2025, StrongDM, 2025. [Online]. Available: https://www.strongdm.com/blog/cloud-security-statistics
[3] Mikel K. Ngueajio et al., “Intrusion Detection Systems Using Support Vector Machines on the KDDCUP’99 and NSL-KDD Datasets: A Comprehensive Survey,” Proceedings of SAI Intelligent Systems Conference, Amsterdam, The Netherlands, pp. 609-629, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Muhammad Azmi Umer et al., “Machine Learning for Intrusion Detection in Industrial Control Systems: Applications, Challenges, and Recommendations,” International Journal of Critical Infrastructure Protection, vol. 38, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Md Liakat Ali et al., “Deep Learning vs. Machine Learning for Intrusion Detection in Computer Networks: A Comparative Study,” Applied Sciences, vol. 15, no. 4, pp. 1-19, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Mudita Uppal et al., “Enhancing Accuracy Through Ensemble Based Machine Learning for Intrusion Detection and Privacy Preservation Over the Network of Smart Cities,” Discover Internet of Things, vol. 5, no. 1, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Chaoyu Zhang et al., “Machine Learning-Based Intrusion Detection Systems: Capabilities, Methodologies, and Open Research Challenges,” Authorea Preprints, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Sunil Kaushik et al., “Robust Machine Learning Based Intrusion Detection System Using Simple Statistical Techniques in Feature Selection,” Scientific Reports, vol. 15, no. 1, pp. 1-20, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Ansam Khraisat et al., “Survey of Intrusion Detection Systems: Techniques, Datasets and Challenges,” Cybersecurity, vol. 2, no. 1, pp. 1-22, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Ahmet Efe, and İrem Nur Abacı, “Comparison of the Host Based Intrusion Detection Systems and Network Based Intrusion Detection Systems,” Celal Bayar University Journal of Science, vol. 18, no. 1, pp. 23-32, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Elena Fedorchenko, Evgenia Novikova, and Anton Shulepov, “Comparative Review of the Intrusion Detection Systems Based on Federated Learning: Advantages and Open Challenges,” Algorithms, vol. 15, no. 7, pp. 1-26, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[12] N-able, Intrusion Detection System (IDS): Signature vs. Anomaly-Based - N-able, 2021. [Online]. Available: https://www.n-able.com/blog/intrusion-detection-system
[13] OTORIO, Intrusion Detection Systems (IDS): Pros and Cons, 2025. [Online]. Available: https://www.otorio.com/blog/intrusion-detection-systems-ids/
[14] Rapid7, The Pros & Cons of Intrusion Detection Systems, 2017. [Online]. Available: https://www.rapid7.com/blog/post/2017/01/11/the-pros-cons-of-intrusion-detection-systems/
[15] Aria Cybersecurity Solutions, Understanding the Strengths and Limitations of Your Intrusion Detection System, 2019. [Online]. Available: https://blog.ariacybersecurity.com/blog/understanding-the-strengths-and-limitations-of-your-intrusion-detection-system
[16] Nari Sivanandam Arunraj et al., “Comparison of Supervised, Semi-supervised and Unsupervised Learning Methods in Network Intrusion Detection System Application,” Applications and Concepts of Business Informatics, no. 6, pp. 10-19, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Pavel Laskov et al., “Learning Intrusion Detection: Supervised or Unsupervised?,” International Conference on Image Analysis and Processing, Cagliari, Italy, pp. 50-57, 2005.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Chuanliang Chen, Yunchao Gong, and Yingjie Tian, “Semi-Supervised Learning Methods for Network Intrusion Detection,” 2008 IEEE International Conference on Systems, Man and Cybernetics, Singapore, pp. 2603-2608, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Richard Kimanzi et al., “Deep Learning Algorithms Used in Intrusion Detection Systems-A Review,” arXiv Preprint, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Muhammad Sajid et al., “Enhancing Intrusion Detection: A Hybrid Machine and Deep Learning Approach,” Journal of Cloud Computing, vol. 13, no. 1, pp. 1-24, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Steven Ning et al., “The Study of Feature Engineering in Machine Learning and Deep Learning for Network Intrusion Detection Systems,” 2024 Silicon Valley Cybersecurity Conference (SVCC), Seoul, Korea, pp. 1-5, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Abiodun Ayantayo et al., “Network Intrusion Detection Using Feature Fusion with Deep Learning,” Journal of Big Data, vol. 10, no. 1, pp. 1-24, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Abdalla Fa Belhagi, and Elshrif Ibrahim Elmurngi, “Intrusion Detection System Using Supervised Learning Techniques,” International Research Journal of Modernization in Engineering Technology and Science, vol. 6, no. 12, pp. 4281-4289, 2024.
[Google Scholar] [Publisher Link]
[24] Shahadat Uddin et al., “Comparing Different Supervised Machine Learning Algorithms for Disease Prediction,” BMC Medical Informatics and Decision Making, vol. 19, no. 1, pp. 1-16, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Tala Talaei Khoei, and Naima Kaabouch, “A Comparative Analysis of Supervised and Unsupervised Models for Detecting Attacks on the Intrusion Detection Systems,” Information, vol. 14, no. 2, pp. 1-14, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Sneha Leela Jacob, and Parveen Sultana Habibullah, “A Systematic Analysis and Review on Intrusion Detection Systems Using Machine Learning and Deep Learning Algorithms,” Journal of Computational and Cognitive Engineering, vol. 4, no. 2, pp. 108-120, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[27] George Sarossy, “Anomaly Detection in Network Data with Unsupervised Learning Methods,” Bachelor Thesis, Mälardalen University, School of Innovation, Design and Engineering, 2021.
[Google Scholar] [Publisher Link]
[28] Prabu Kaliyaperumal et al., “Harnessing DBSCAN and Auto-Encoder for Hyper Intrusion Detection in Cloud Computing,” Bulletin of Electrical Engineering and Informatics, vol. 13, no. 5, pp. 3345-3354, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Woo-Hyun Choi, and Jongwon Kim, “Unsupervised Learning Approach for Anomaly Detection in Industrial Control Systems,” Applied System Innovation, vol. 7, no. 2, pp. 1-16, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Taehoon Kim, and Wooguil Pak, “Deep Learning-Based Network Intrusion Detection Using Multiple Image Transformers,” Applied Sciences, vol. 13, no. 5, pp. 1-15, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Sydney Mambwe Kasongo, “A Deep Learning Technique for Intrusion Detection System Using a Recurrent Neural Networks based Framework,” Computer Communications, vol. 199, pp. 113-125, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[32] Zhenyue Long et al., “A Transformer-Based Network Intrusion Detection Approach for Cloud Security,” Journal of Cloud Computing, vol. 13, no. 1, pp. 1-11, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[33] Estabraq Saleem Abduljabbar Alars, and Sefer Kurnaz, “Enhancing Network Intrusion Detection Systems with Combined Network and Host Traffic Features Using Deep Learning: Deep Learning and IoT Perspective,” Discover Computing, vol. 27, no. 1, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[34] Mohammed F. Suleiman, and Biju Issac, “Performance Comparison of Intrusion Detection Machine Learning Classifiers on Benchmark and New Datasets,” 2018 28th International Conference on Computer Theory and Applications, Alexandria, Egypt, pp. 19-23, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[35] Fatima Isiaka, “Performance Metrics of an Intrusion Detection System through Window-Based Deep Learning Models,” Journal of Data Science and Intelligent Systems, vol. 2, no. 3, pp. 174-180, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[36] Sudhanshu Sekhar Tripathy, and Bichitrananda Behera, “Performance Evaluation of Machine Learning Algorithms for Intrusion Detection System,” Cryptology ePrint Archive, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[37] Syed Ali Raza Shah, and Biju Issac, “Performance Comparison of Intrusion Detection Systems and Application of Machine Learning to Snort System,” Future Generation Computer Systems, vol. 80, pp. 157-170, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[38] Sydney M. Kasongo, and Yanxia Sun, “Performance Analysis of Intrusion Detection Systems Using a Feature Selection Method on the UNSW-NB15 Dataset,” Journal of Big Data, vol. 7, no. 1, pp. 1-20, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[39] Ibtihal Mohammed Khaleel, “A Comparative Performance Evaluation of Network Intrusion Detection Using Machine and Deep Learning Algorithms,” Mathematics for Applications, vol. 13, no. 2, pp. 1-12, 2024.
[Google Scholar] [Publisher Link]
[40] Chadia E. L. Asry et al., “A Robust Intrusion Detection System Based on a Shallow Learning Model and Feature Extraction Techniques,” PlosOne, vol. 19, no. 1, pp. 1-31, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[41] Yaping Chang, Wei Li, and Zhongming Yang, “Network Intrusion Detection Based on Random Forest and Support Vector Machine,” 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing, Guangzhou, China, pp. 635-638, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[42] Kang Leng Chiew, and Bian Hui, “An Improved Network Intrusion Detection Method Based on CNN-LSTM-SA,” Journal of Advanced Research in Applied Sciences and Engineering Technology, vol. 44, no. 1, pp. 225-238, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[43] Hamza Kheddar, “Transformers and Large Language Models for Efficient Intrusion Detection Systems: A Comprehensive Survey,” arXiv Preprint, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[44] V. Kantharaju et al., “Machine Learning Based Intrusion Detection Framework for Detecting Security Attacks in Internet of Things,” Scientific Reports, vol. 14, no. 1, pp. 1-10, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[45] Shaashwat Agrawal et al., “Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directions,” arXiv Preprint, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[46] Brunel Rolack Kikissagbe, and Meddi Adda, “Machine Learning-Based Intrusion Detection Methods in IoT Systems: A Comprehensive Review,” Electronics, vol. 13, no. 18, pp. 1-23, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[47] Huda Ali Alatwi, and Charles Morisset, “Adversarial Machine Learning in Network Intrusion Detection Domain: A Systematic Review,” arXiv Preprint, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[48] Diogo Gaspar, Paulo Silva, and Catarina Silva, “Explainable AI for Intrusion Detection Systems: LIME and SHAP Applicability on Multi-Layer Perceptron,” IEEE Access, vol. 12, pp. 30164-30175, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[49] Abeer Alalmaie, Priyadarsi Nanda, and Xiangjian He, “ZT-NIDS: Zero Trust, Network Intrusion Detection System,” Proceedings of the 20th International Conference on Security and Cryptography SECRYPT, Rome, Italy, vol. 1, pp. 99-110, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[50] Ahmed Abubakar Aliyu, Jinshuo Liu, and Ezekia Gilliard, “A Decentralized and Self-Adaptive Intrusion Detection Approach Using Continuous Learning and Blockchain Technology,” Journal of Data Science and Intelligent Systems, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[51] Anna L. Buczak, and Erhan Guven, “A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection,” IEEE Communications Surveys & Tutorials, vol. 18, no. 2, pp. 1153-1176, 2015.
[CrossRef] [Google Scholar] [Publisher Link]