Intelligent Cloud-based Intrusion Detection System Using Enhanced Sunflower Optimization with Deep Learning Model
Intelligent Cloud-based Intrusion Detection System Using Enhanced Sunflower Optimization with Deep Learning Model |
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| © 2023 by IJETT Journal | ||
| Volume-71 Issue-10 |
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| Year of Publication : 2023 | ||
| Author : Samineni Nagamani, S. Arivalagan, M. Senthil, P. Sudhakar |
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| DOI : 10.14445/22315381/IJETT-V71I10P210 | ||
How to Cite?
Samineni Nagamani, S. Arivalagan, M. Senthil, P. Sudhakar, "Intelligent Cloud-based Intrusion Detection System Using Enhanced Sunflower Optimization with Deep Learning Model," International Journal of Engineering Trends and Technology, vol. 71, no. 10, pp. 105-114, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I10P210
Abstract
Cloud Computing (CC) can be used in many research areas mainly for its network capacity and high computing power. Flexibility, Data security, and cost-effectiveness of working options for remote employees made this technology grab the interest of many. Intruders find innovative attack types every day; thus, to thwart such attacks, initial accurate detection should be done using Intrusion Detection Systems (IDSs), and after that, proper responses must be given. IDSs, which serve a most significant role in the security of the network, contain 3 main elements they are decision engine, data collection, and conversion or feature selection. Currently, DL was developed as a novel technique which enables a high accuracy rate and low training time with its distinctive learning system. Therefore, this study develops an Intelligent Cloud based Intrusion Detection System using an Enhanced Sunflower Optimization with Deep Learning (CIDS-ESFODL) model. The presented CIDS-ESFODL technique focuses on the recognition and categorization of intrusions in the cloud platform. The presented CIDS-ESFODL model has a three-phase process. In the initial stage, the ESFO algorithm is applied as a feature selector, providing an optimal subset of features. Secondly, the Denoising Autoencoder (DAE) technique is implemented for classifying and recognizing intrusions. Finally, the Nadam optimizer is utilized for the adjustment of the hyperparameters. The investigational validation of the CIDS-ESFODL technique on the benchmark IDS dataset reported its significant performance over the other current models by means of distinct measures.
Keywords
Cloud environment, Deep learning, Nadam optimizer, Intrusion Detection System, Feature selection.
References
[1] Jan Lansky et al., “Deep Learning-Based Intrusion Detection Systems: A Systematic Review,” IEEE Access, vol. 9, pp.101574-101599, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Mesut Uğurlu, and İbrahim Alper Doğru, “A Survey on Deep Learning Based Intrusion Detection System,” 4th International Conference on Computer Science and Engineering (UBMK), pp. 223-228, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Mohamed Amine Ferrag et al., “Deep Learning-Based Intrusion Detection for Distributed Denial of Service Attack in Agriculture 4.0,” Electronics, vol. 10, no. 11, pp. 1-26, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Jiangang Shu et al., “Collaborative Intrusion Detection for VANETs: A Deep Learning-Based Distributed SDN Approach,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 7, pp. 4519-4530, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Junjie Cen, and Yongbo Li, “Deep Learning-Based Anomaly Traffic Detection Method in Cloud Computing Environment,” Wireless Communications and Mobile Computing, vol. 2022, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Loheswaran Karuppusamy et al., “Chronological Salp Swarm Algorithm based Deep Belief Network for Intrusion Detection in Cloud using Fuzzy Entropy,” International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, vol. 35, no. 1, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Parul Singh, and Virender Ranga, “Attack and Intrusion Detection in Cloud Computing using an Ensemble Learning Approach,” International Journal of Information Technology, vol. 13, no. 2, pp. 565-571, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Nishtha Srivastava et al., “A Review of Machine Learning-Based Intrusion Detection Systems on the Cloud,” Security, Privacy and Data Analytics, vol. 848, pp. 303-317, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[9] E. Balamurugan et al., “Network Optimization using Defender System in Cloud Computing Security based Intrusion Detection System with Game Theory Deep Neural Network (IDSGT-DNN),” Pattern Recognition Letters, vol. 156, pp. 142-151, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Vinayakumar Ravi, Rajasekhar Chaganti, and Mamoun Alazab, “Recurrent Deep Learning-Based Feature Fusion Ensemble Meta-Classifier Approach for Intelligent Network Intrusion Detection System,” Computers and Electrical Engineering, vol. 102, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[11] M. Mayuranathan, M. Murugan, and V. Dhanakoti, “Best Features Based Intrusion Detection System by RBM Model for Detecting DDoS in Cloud Environment,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 3, pp. 3609-3619, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Anil Kumar et al., “An Intrusion Identification and Prevention for Cloud Computing: From the Perspective of Deep Learning,” Optik, vol. 270, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Wisam Elmasry, Akhan Akbulut, and Abdul Halim Zaim, “A Design of an Integrated Cloud-Based Intrusion Detection System with Third Party Cloud Service,” De Gruyter Open Access, vol. 11, no. 1, pp. 365-379, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Andrea Sharon et al., “An Intelligent Intrusion Detection System using Hybrid Deep Learning Approaches in Cloud Environment,” International Conference on Computer, Communication, and Signal Processing, pp. 281-298, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[15] S. Krishnaveni et al., “Efficient Feature Selection and Classification through Ensemble Method for Network Intrusion Detection on Cloud Computing,” Cluster Computing, vol. 24, no, 3, pp. 1761-1779, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Hongpo Zhang et al., “An Effective Deep Learning Based Scheme for Network Intrusion Detection,” 24th International Conference on Pattern Recognition (ICPR), pp. 682-687, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Hojjat Emami, “Cloud Task Scheduling using Enhanced Sunflower Optimization Algorithm,” ICT Express, vol. 8, no. 1, pp. 97-100, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Pradeep Kumar Mallick et al., “Brain MRI Image Classification for Cancer Detection using Deep Wavelet Autoencoder-Based Deep Neural Network,” IEEE Access, vol. 7, pp. 46278-46287, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Bin Xiao, Yonggui Liu, and Bing Xiao, “Accurate State-of-Charge Estimation Approach for Lithium-Ion Batteries by Gated Recurrent Unit with Ensemble Optimizer,” IEEE Access, vol. 7, pp. 54192-54202, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Mohammad Dawood Momand, Vikas Thada, and Utpal Shrivastava, “Intrusion Detection System in IoT Network,” SSRG International Journal of Computer Science and Engineering, vol. 7, no. 4, pp. 11-15, 2020.
[CrossRef] [Publisher Link]
[21] Muataz Salam Al-Daweri et al., “An Analysis of the KDD99 and UNSW-NB15 Datasets for the Intrusion Detection System,” Symmetry, vol. 12, no. 10, pp. 1-32, 2020.
[CrossRef] [Google Scholar] [Publisher Link]