Abstract
The use of the Internet is enhanced drastically in the current era, which connects multiple computers in a network and a group of devices. In addition, every sector uses the Internet to communicate and send data digitally. However, the Internet is affected due to unwanted activities and cyber-attacks by attackers. Hence, intrusion detection systems have recently been used to detect incoming attacks. Therefore, the present study has designed and developed the intrusion detection scheme for cloud computing through ensemble learning and a feature selection approach. The proposed system is tested on NSL-KDD datasets. The critical features were selected from the dataset, and dimensionality was reduced using feature selection methods. The ensemble learning approach combined the single process to generate the robust way and successfully confirmed with high accuracy and negligible error rate. Two machine learning methods, such as decision tree and Naïve Bayes, have been used in training the ensemble learning models. The overall accuracy was 90 and 99%, with 9.61 and 0.21% error rates for Naïve Bayes and decision tree classifier, respectively. The present study can successfully detect network attacks and secure cloud-based platforms. The proposed approach is more stable and more accurate than the earlier research.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.
References
Ramachandran M, Chang V. Towards performance evaluation of cloud service providers for cloud data security. Int J Inf Manage. 2016;36(4):618–25.
Arjunan K, Modi CN (2017) An enhanced intrusion detection framework for securing network layer of cloud computing. In: 2017 ISEA Asia security and privacy (ISEASP) (pp. 1–10). IEEE.
Butt UA, Mehmood M, Shah SBH, Amin R, Shaukat MW, Raza SM, Piran M. A review of machine learning algorithms for cloud computing security. Electronics. 2020;9(9):1379.
Krishnaveni S, Sivamohan S, Sridhar SS, Prabakaran S. Efficient feature selection and classification through ensemble method for network intrusion detection on cloud computing. Clust Comput. 2021;24(3):1761–79.
Padmakumari P, Surendra K, Sowmya M, Sravya M. Effective intrusion detection system for cloud architecture. ARPN J Eng Appl Sci. 2014;9(11):2135–9.
Khraisat A, Gondal I, Vamplew P, Kamruzzaman J, Alazab A. Hybrid intrusion detection system based on the stacking ensemble of c5 decision tree classifier and one class support vector machine. Electronics. 2020;9(1):173.
Krishnaveni S, Prabakaran S. Ensemble approach for network threat detection and classification on cloud computing. Concurrency Comput. 2021;33(3): e5272.
Modi CN, Patel DR, Patel A, Muttukrishnan R (2012) Bayesian classifier and snort based network intrusion detection system in cloud computing. In: 2012 Third international conference on computing, communication and networking technologies (ICCCNT'12) (pp. 1–7). IEEE.
Modi CN, Patel DR, Patel A, Rajarajan M. Integrating signature apriori based network intrusion detection system (NIDS) in cloud computing. Proc Technol. 2012;6:905–12.
Aljamal I, Tekeoğlu A, Bekiroglu K, Sengupta S (2019) Hybrid intrusion detection system using machine learning techniques in cloud computing environments. In: 2019 IEEE 17th international conference on software engineering research, management and applications (SERA) (pp 84–89). IEEE.
Kholidy HA, Erradi A (2015) A cost-aware model for risk mitigation in Cloud computing systems. In: 2015 IEEE/ACS 12th international conference of computer systems and applications (AICCSA) (pp. 1–7). IEEE.
Idhammad M, Afdel K, Belouch M. Distributed intrusion detection system for cloud environments based on data mining techniques. Proc Comput Sci. 2018;127:35–41.
Wani AR, Rana QP, Saxena U, Pandey N (2019) Analysis and detection of DDoS attacks on cloud computing environment using machine learning techniques. In: 2019 amity international conference on artificial intelligence (AICAI) (pp. 870–875). IEEE.
Mahmood HA. Network intrusion detection system (NIDS) in cloud environment based on hidden Naïve Bayes multiclass classifier. Al-Mustansiriyah J Sci. 2017;28(2):134–42.
Besharati E, Naderan M, Namjoo E. LR-HIDS: logistic regression host-based intrusion detection system for cloud environments. J Ambient Intell Humaniz Comput. 2019;10(9):3669–92.
Masoodi F. Machine learning for classification analysis of intrusion detection on NSL-KDD dataset. Turkish J Comput Math Educ (TURCOMAT). 2021;12(10):2286–93.
Farhat S, Abdelkader M, Meddeb-Makhlouf A, Zarai F (2020) Comparative study of classification algorithms for cloud ids using nsl-kdd dataset in weka. In: 2020 International Wireless Communications and Mobile Computing (IWCMC) (pp. 445–450). IEEE.
Yihunie F, Abdelfattah E, Regmi A (2019) Applying machine learning to anomaly-based intrusion detection systems. In: 2019 IEEE long island systems, applications and technology conference (LISAT) (pp. 1–5). IEEE.
Abrar I, Ayub Z, Masoodi F, Bamhdi AM (2020) A machine learning approach for intrusion detection system on NSL-KDD dataset. In: 2020 international conference on smart electronics and communication (ICOSEC) (pp. 919–924). IEEE.
Revathi S, Malathi A. A detailed analysis on NSL-KDD dataset using various machine learning techniques for intrusion detection. Int J Eng Res Technol (IJERT). 2013;2(12):1848–53.
Rokade MD, Sharma YK (2021) MLIDS: a machine learning approach for intrusion detection for real time network dataset. In: 2021 International conference on emerging smart computing and informatics (ESCI) (pp. 533–536). IEEE.
Yedukondalu G, Bindu GH, Pavan J, Venkatesh G, SaiTeja A (2021) Intrusion detection system framework using machine learning. In: 2021 third international conference on inventive research in computing applications (ICIRCA) (pp. 1224–1230). IEEE.
Bagyalakshmi C, Samundeeswari ES (2020) DDoS attack classification on cloud environment using machine learning techniques with different feature selection methods. Int J 9(5)
Guezzaz A, Benkirane S, Azrour M, Khurram S. A reliable network intrusion detection approach using decision tree with enhanced data quality. Secur Commun Netw. 2021;2021:1–8.
Megantara AA, Ahmad T. A hybrid machine learning method for increasing the performance of network intrusion detection systems. J Big Data. 2021;8(1):1–19.
Acknowledgements
Manuscript Communication Number (MCN): IU/R&D/2022–MCN0001723 office of research and development, Integral University, Lucknow, UP, India. The authors are thankful to the Canadian Institute for Cybersecurity (CIC) University of New Brunswick, Canada for providing the NSL-KDD datasets.
Funding
Not applicable.
Author information
Authors and Affiliations
Contributions
MK: data curation, methodology, experiments, writing—original draft, and review & final editing. MH: correction and guidance. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Conflict of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article is part of the topical collection “Soft Computing in Engineering Applications” guest edited by Kanubhai K. Patel
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Khan, M., Haroon, M. Detecting Network Intrusion in Cloud Environment Through Ensemble Learning and Feature Selection Approach. SN COMPUT. SCI. 5, 84 (2024). https://doi.org/10.1007/s42979-023-02390-z
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s42979-023-02390-z