XGBoost-Based Intelligent Intrusion Detection for Secure Cloud Computing Using Correlation-Based Feature Selection

Rohith Reddy Mandala; Bhagath Singh Jayaprakasam; Narsing Rao Dyavani; Charles Ubagaram; Venkat Garikipati; Soundarraj K

doi:10.53941/aiet.2026.100013

Abstract

Cloud Computing has emerged as an essential platform for the modern applications. But the growing complexity of the attacks puts pressure on the security of cloud environments. Current intrusion detection systems (IDS) have weaknesses of detection inaccuracy, high false alarm rate and low efficiency in processing high-dimensional network traffic data. To overcome these drawbacks, this study suggests an intelligent intrusion detection framework based on XGBoost algorithm with correlation-based feature selection to remove redundant features, decrease the computational complexity, and enhance the classification performance. The proposed framework is based on UNSW-NB15 dataset, which consists of realistic normal and malicious network traffic, and data preprocessing methods such as categorical encoding, feature scaling, and a 70:30 ratio for training and testing. The results of this experimental study show that the proposed model attains 99.11% accuracy, 99.13% precision, 99.57% recall, 99.35% F1 score and 99.84% AUC which is higher than that achieved by the traditional machine learning models like random forest, decision tree, naïve bayes etc. The results show that the proposed framework is accurate, efficient and scalable solution for intelligent cloud intrusion detection which improves the security and reliability of cloud computing environments.

References

1.
Marston, S.; Li, Z.; Bandyopadhyay, S.; et al. Cloud computing—The business perspective. Decis. Support Syst. 2011, 51, 176–189. https://doi.org/10.1016/j.dss.2010.12.006.
2.
Habib, G.; Sharma, S.; Ibrahim, S.; et al. Blockchain Technology: Benefits, Challenges, Applications, and Integration of Blockchain Technology with Cloud Computing. Future Internet 2022, 14, 341. https://doi.org/10.3390/fi14110341.
3.
Abdulsalam, Y.S.; Hedabou, M. Security and Privacy in Cloud Computing: Technical Review. Future Internet 2022, 14, 11. https://doi.org/10.3390/fi14010011.
4.
Senthilkumar, G.; Tamilarasi, K.; Periasamy, J.K. Cloud intrusion detection framework using variational auto encoder Wasserstein generative adversarial network optimized with archerfish hunting optimization algorithm. Wirel. Netw. 2024, 30, 1383–1400. https://doi.org/10.1007/s11276-023-03571-7.
5.
Mazhar, T.; Irfan, H.M.; Khan, S.; et al. Analysis of Cyber Security Attacks and Its Solutions for the Smart grid Using Machine Learning and Blockchain Methods. Future Internet 2023, 15, 83. https://doi.org/10.3390/fi15020083.
6.
Walling, S.; Lodh, S. Enhancing IoT intrusion detection through machine learning with AN-SFS: A novel approach to high performing adaptive feature selection. Discov. Internet Things 2024, 4, 16. https://doi.org/10.1007/s43926-024-00074-5.
7.
Padmaja, M.; Shitharth, S. Grow of Artificial Intelligence to Challenge Security in IoT Application. Wirel. Pers. Commun. 2022, 127, 1829–1845. https://doi.org/10.1007/s11277-021-08725-4.
8.
Jablaoui, R.; Liouane, N. GA-CNN-BiGRU-IDS: A robust framework for Intrusion Detection System based on GA for data augmentation and hybrid CNN-BiGRU model for spatiotemporal feature extraction. Comput. Electr. Eng. 2026, 130, 110900. https://doi.org/10.1016/j.compeleceng.2025.110900.
9.
Elsadig, M.; Ibrahim, A.O.; Basheer, S.; et al. Intelligent Deep Machine Learning Cyber Phishing URL Detection Based on BERT Features Extraction. Electronics 2022, 11, 3647. https://doi.org/10.3390/electronics11223647.
10.
Angel, N.A.; Ravindran, D.; Vincent, P.M.D.R.; et al. Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies. Sensors 2022, 22, 196. https://doi.org/10.3390/s22010196.
11.
Suvarna, M.; Yap, K.S.; Yang, W.; et al. Cyber–Physical Production Systems for Data-Driven, Decentralized, and Secure Manufacturing—A Perspective. Engineering 2021, 7, 1212–1223. https://doi.org/10.1016/j.eng.2021.04.021.
12.
Humayun, M.; Alsirhani, A.; Alserhani, F.; et al. Transformative synergy: SSEHCET—Bridging mobile edge computing and AI for enhanced eHealth security and efficiency. J. Cloud Comput. 2024, 13, 37. https://doi.org/10.1186/s13677-024-00602-2.
13.
Abdullayeva, F. Cyber resilience and cyber security issues of intelligent cloud computing systems. Results Control. Optim. 2023, 12, 100268. https://doi.org/10.1016/j.rico.2023.100268.
14.
Nankya, M.; Chataut, R.; Akl, R. Securing Industrial Control Systems: Components, Cyber Threats, and Machine Learning-Driven Defense Strategies. Sensors 2023, 23, 8840. https://doi.org/10.3390/s23218840.
15.
Magara, T.; Zhou, Y. Internet of Things (IoT) of Smart Homes: Privacy and Security. J. Electr. Comput. Eng. 2024, 2024, 7716956. https://doi.org/10.1155/2024/7716956.
16.
Buyya, R.; Ilager, S.; Arroba, P. Energy-efficiency and sustainability in new generation cloud computing: A vision and directions for integrated management of data centre resources and workloads. Softw. Pract. Exp. 2024, 54, 24–38. https://doi.org/10.1002/spe.3248.
17.
Onyema, E.M.; Dalal, S.; Romero, C.A.T.; et al. Design of Intrusion Detection System based on Cyborg intelligence for security of Cloud Network Traffic of Smart Cities. J. Cloud Comput. 2022, 11, 26. https://doi.org/10.1186/s13677-022-00305-6.
18.
Kamruzzaman, M.M.; Alruwaili, O. AI-based computer vision using deep learning in 6G wireless networks. Comput. Electr. Eng. 2022, 102, 108233. https://doi.org/10.1016/j.compeleceng.2022.108233.
19.
Bacanin, N.; Zivkovic, T.; Zivkovic, M.; et al. Intrusion Detection for Smart City Security by Boosting Algorithms Optimized by Metaheuristics Algorithm. In Proceedings of the 2025 17th International Conference on Advanced Technologies, Systems and Services in Telecommunications (TELSIKS), Nis, Serbia, 22–24 October 2025; pp. 28–33. https://doi.org/10.1109/telsiks65061.2025.11240640.
20.
Antonijevic, M.; Zivkovic, M.; Djuric Jovicic, M.; et al. Intrusion detection in metaverse environment internet of things systems by metaheuristics tuned two level framework. Sci. Rep. 2025, 15, 3555. https://doi.org/10.1038/s41598-025-88135-9.
21.
Dakic, P.; Zivkovic, M.; Jovanovic, L.; et al. Intrusion detection using metaheuristic optimization within IoT/IIoT systems and software of autonomous vehicles. Sci. Rep. 2024, 14, 22884. https://doi.org/10.1038/s41598-024-73932-5.
22.
Savanovic, N. Hybrid CNN XGBoost intrusion detection approach tuned by modified sine cosine algorithm towards better cloud security. Connect. Sci. 2025, 37, 2549581. https://doi.org/10.1080/09540091.2025.2549581.
23.
Cvetkovic, D.; Zivkovic, M.; Zivkovic, T.; et al. CatBoost Optimized with Adapted Reptile Search Algorithm for Intrusion Detection Systems. In Proceedings of the 2025 33rd Telecommunications Forum (TELFOR), Belgrade, Serbia, 25–26 November 2025; pp. 1–4. https://doi.org/10.1109/telfor67910.2025.11314223.
24.
Rabbani, M.; Wang, Y.; Khoshkangini, R.; et al. A Review on Machine Learning Approaches for Network Malicious Behavior Detection in Emerging Technologies. Entropy 2021, 23, 529. https://doi.org/10.3390/e23050529.
25.
Farhangi, F. Investigating the role of data preprocessing, hyperparameters tuning, and type of machine learning algorithm in the improvement of drowsy EEG signal modeling. Intell. Syst. Appl. 2022, 15, 200100. https://doi.org/10.1016/j.iswa.2022.200100.
26.
Simaiya, S.; Lilhore, U.K.; Sharma, Y.K.; et al. A hybrid cloud load balancing and host utilization prediction method using deep learning and optimization techniques. Sci. Rep. 2024, 14, 1337. https://doi.org/10.1038/s41598-024-51466-0.
27.
Ghorbani, A.A. FIGS: A Realistic Intrusion-Detection Framework for Highly Imbalanced IoT Environments. Available online: https://www.mdpi.com/2079-9292/14/14/2917 (accessed on 27 December 2025).

Scilight Press

Author Information

Abstract

Keywords

References

About Scilight

Journals

Publishing Policies

Contact Us