Abstract
Nowadays, digital data is an important asset for every organization. With the advent of cloud computing, cloud service providers (CSPs) offer the required infrastructure to end-users for storage and provide flexibility in accessing data. Since the users access the data from the cloud through the Internet, the data stored in the cloud are exposed to various intrusions. Intrusion detection is considered to be a significant issue in the cloud. The existing techniques are capable to detect well-known attacks but fall short in detecting low frequent attacks. To address this issue, we propose a novel intrusion detection system (IDS) in the cloud using a combination of kernel fuzzy c-means clustering (KFCM) and an optimal type-2 fuzzy neural network (OT2FNN). To achieve this, we optimally select the parameters of T2FNN using the lion optimization algorithm (LOA) for weight optimization. The proposed IDS detects the intrusion and allow only normal data to be stored in the cloud. Simulation results on the NSL-KDD dataset show that the proposed IDS system gives better results than the existing IDS systems in terms of precision, recall, and F-measure.
Similar content being viewed by others
References
Vieira, K., Schulter, A., Westphall, C., Westphall, C.: Intrusion detection techniques in grid and cloud computing environment. IEEE IT Prof. Mag. 2010, 38–43 (2010)
Hai, J., Guofu, X., Deqing, Z.: AVMM-based intrusion prevention system in cloud computing environment. J. Supercomput. 66(3), 1133–1151 (2013)
Oktay, U., Sahingoz, O.K.: Attack types and intrusion detection systems in cloud computing. In: Proceedings of 6th International Information Security & Cryptology Conference, pp. 71–76 (2013)
Raja, S., Ramaiah, S.: An efficient fuzzy-based hybrid system to cloud intrusion detection. Int. J. Fuzzy Syst. 19(1), 62–77 (2017)
Work, W.T.: Intrusion Detection Systems (IDS). National Institute of Standers and Technology (2003)
Kumbhare, A., Chaudhari, M.: IDS: survey on intrusion detection system in cloud computing. IJCSMC 3(4), 497–502 (2014)
Roschke, S., Cheng, F., Meinel, C.: Intrusion Detection in the Cloud. In: Eighth IEEE international conference on dependable, autonomic and secure computing (2009).
Balamurugan, V., Saravanan, R.: Enhanced intrusion detection and prevention system on cloud environment using hybrid classification and OTS generation. Clust. Comput. 22(6), 13027–13039 (2019)
Krishnaveni, S., Sivamohan, S., Sridhar, S.S., Prabakaran, S.: Efficient feature selection and classification through ensemble method for network intrusion detection on cloud computing. Clust. Comput. 181, 1–19 (2021)
Manickam, M., Rajagopalan, S.P.: A hybrid multi-layer intrusion detection system in cloud. Clust. Comput. 22(2), 3961–3969 (2019)
Jaber, A.N., Rehman, S.U.: FCM–SVM based intrusion detection system for cloud computing environment. Clust. Comput. 23, 1–11 (2020)
Velliangiri, S., Premalatha, J.: Intrusion detection of distributed denial of service attack in cloud. Clust. Comput. 22(5), 10615–10623 (2019)
Raman, M.G., Somu, N., Jagarapu, S., Manghnani, T., Selvam, T., Krithivasan, K., Sriram, V.S.: An efficient intrusion detection technique based on support vector machine and improved binary gravitational search algorithm. Artif. Intell. Rev. 53, 1–32 (2019)
Gao, Y., Liu, Y., Jin, Y., Chen, J., Wu, H.: A novel semi-supervised learning approach for network intrusion detection on cloud-based robotic system. IEEE Access 6, 50927–50938 (2018)
Devi, R.R., Chamundeeswari, V.V.: Triple DES: privacy preserving in big data healthcare. Int. J. Parallel Prog. 48(3), 515–533 (2020)
Abd EL-Latif, A.A., Abd-El-Atty, B., Venegas-Andraca, S.E., Mazurczyk, W.: Efficient quantum-based security protocols for information sharing and data protection in 5G networks. Fut. Gener. Comput. Syst. 100, 893–906 (2019)
AbdEl-Latif, A.A., Abd-El-Atty, B., Hossain, M.S., Elmougy, S., Ghoneim, A.: Secure quantum steganography protocol for fog cloud Internet of Things. IEEE Access 6, 10332–10340 (2018)
Hajimirzaei, B., Navimipour, N.J.: Intrusion detection for cloud computing using neural networks and artificial bee colony optimization algorithm. ICT Express 5(1), 56–59 (2019)
Mahmood, Z., Agrawal, C.: Intrusion detection in cloud computing environment using neural network. Int. J. Res. Comput. Eng. Electron. 1, 1 (2014)
Moradi, M., Zulkernine, M.: A neural network based system for intrusion detection and classification of attacks. IEEE International Conference on Advances in Intelligent System (2004)
Manickam, M., Ramaraj, N., Chellappan, C.: A combined PFCM and recurrent neural network based IDS for cloud environment. Int. J. Bus. Intell. Data Mining 1, 1 (2017)
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Srilatha, D., Shyam, G.K. Cloud-based intrusion detection using kernel fuzzy clustering and optimal type-2 fuzzy neural network. Cluster Comput 24, 2657–2672 (2021). https://doi.org/10.1007/s10586-021-03281-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10586-021-03281-9