Cloud Computing (CC) is a relatively new technology that allows for widespread access and storage on the internet. Despite its low cost and numerous benefits, cloud technology still confronts several obstacles, including data loss, quality concerns, and data security like recurring hacking. The security of data stored in the cloud has become a major worry for both Cloud Service Providers (CSPs) and users. As a result, a powerful Intrusion Detection System (IDS) must be set up to detect and prevent possible cloud threats at an early stage. Intending to develop a novel IDS system, this paper introduces a new optimization concept named Lion Mutated-Genetic Algorithm (LM-GA) with the hybridization of Machine Learning (ML) algorithms such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). Initially, the input text data is preprocessed and balanced to avoid redundancy and vague data. The preprocessed data is then subjected to the hybrid Deep Learning (DL) models namely the CNN-LSTM model to get the IDS output. Now, the intruded are discarded and non-intruded data are secured using Advanced Encryption Standard (AES) encryption model. Besides, the optimal key selection is done by the proposed LM-GA model and the cipher text is further secured via the steganography approach. NSL-KDD and UNSW-NB15 are the datasets used to verify the performance of the proposed LM-GA-based IDS in terms of average intrusion detection rate, accuracy, precision, recall, and F-Score.
Keywords
Cloud Security, Intrusion Detection System, Lion Mutated-Genetic Algorithm, Convolutional Neural Network, Long Short-Term Memory, Hybrid Deep Learning.
S. Reddy, S. c. Krishna, A. Mahendra and N. Neelima, "Providing Security to private cloud Using AES Algorithm," Third International Conference on Inventive Research in Computing Applications (ICIRCA), 2021, pp. 1-5.
R. U. Khan X. Zhang M. Alazab and R. Kumar "An improved convolutional neural network model for intrusion detection in networks". Proc. Cybersecurity Cyberforensics Conf. (CCC), May 2019, pp. 74-77.
S. Soheily-Khah P. Marteau and N. Béchet "Intrusion detection in network systems through hybrid supervised and unsupervised machine learning process: A case study on the ISCX dataset", Proc. 1st Int. Conf. Data Intell. Secur. (ICDIS), Apr 2018, pp. 219-226.
N. Suganya, R. Sathiya, G. Ilamurugan, M. Pavithra and C. Karthikeyan, "Enhancing the Reliability of Cloud Data by Implementing AES Algorithm", 5th International Conference on Intelligent Computing and Control Systems (ICICCS), 2021, pp. 90-95.
K. Kalaiselvi and A. Kumar, "Enhanced AES cryptosystem by using genetic algorithm and neural network in S-box", IEEE International Conference on Current Trends in Advanced Computing (ICCTAC), 2016, pp. 1-6.
K. Jaspin, S. Selvan, S. Sahana and G. Thanmai, "Efficient and Secure File Transfer in Cloud Through Double Encryption Using AES and RSA Algorithm," International Conference on Emerging Smart Computing and Informatics (ESCI), 2021.
P. Sivakumar, M. NandhaKumar, R. Jayaraj and A. S. Kumaran, "Securing Data and Reducing the Time Traffic Using AES Encryption with Dual Cloud", 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), 2019, pp. 1-5.
Daoud L, Hussein F and Rafla N, “Optimization of advanced encryption standard (AES) using vivado high level synthesis (HLS)”, In Proceedings of the 34th International Conference on Computers and their Applications, 2019, pp. 36–44.
Modi, C.N., and Acha, K, “Virtualization layer security challenges and intrusion detection/prevention systems in cloud computing: a comprehensive review”, J Supercomput, 2017, pp. 1192–1234.
O. Alkadi, N. Moustafa, B. Turnbull and K. -K. R. Choo, "A Deep Blockchain Framework-Enabled Collaborative Intrusion Detection for Protecting IoT and Cloud Networks", IEEE Internet of Things Journal, vol. 8, no. 12, 15 June 2021, pp. 9463-9472.
M. Nadeem, A. Arshad, S. Riaz, S. S. Band and A. Mosavi, "Intercept the Cloud Network From Brute Force and DDoS Attacks via Intrusion Detection and Prevention System," in IEEE Access, vol. 9, 2021, pp. 152300-152309.
L. Kacha and Abdelhafi Zitouni "An OverView on Data Security in Cloud Computing", Cybern. Approaches Intell Syst. vol. 661, 2017, pp. 250-261.
Yung Ming and Lily Yuan, “Supervised Learning Methods and Applications in Medical Research”, Journal of Computing and Natural Science, vol.2, no.1, pp. 027-034, January 2022. doi: 10.53759/181X/JCNS202202005.
Y. Gao, Y. Liu, Y. Jin, J. Chen and H. Wu, "A Novel Semi-Supervised Learning Approach for Network Intrusion Detection on Cloud-Based Robotic System", IEEE Access, vol. 6, 2018, pp. 50927-50938.
G. Loukas, T. Vuong, R. Heartfield, G. Sakellari, Y. Yoon and D. Gan, "Cloud-Based Cyber-Physical Intrusion Detection for Vehicles Using Deep Learning", IEEE Access, vol. 6, 2018, pp. 3491-3508.
Deshpande, P., Sharma, S.C., Peddoju, S.K, “HIDS: A host based intrusion detection system for cloud computing environment”, Int J Syst Assur Eng Manag, vol. 9, 2018, pp. 567–576.
Jaber, A.N., Rehman, S.U. “FCM–SVM based intrusion detection system for cloud computing environment”, Cluster Comput, vol. 23, 2020, pp. 3221–3231.
Krishnaveni, S., Sivamohan, S., Sridhar, S.S. et al. “Efficient feature selection and classification through ensemble method for network intrusion detection on cloud computing”, Cluster Comput, vol. 24, 2021, pp. 1761–1779.
Wei, P., Li, Y., Zhang, Z., Hu, T., Li, Z., & Liu, D. “An optimization method for intrusion detection classification model based on deep belief network”, IEEE Access, vol. 7, 2019, pp. 87593–87605.
M. Mazini B. Shirazi and I. Mahdavi, "Anomaly network-based intrusion detection system using a reliable hybrid artificial bee colony and AdaBoost algorithms", J. King Saud Univ.-Comput. Inf. Sci, vol. 31, no. 4, October 2019, pp. 541-553.
P. Narwal D. Kumar and S. N. Singh "A hidden Markov model combined with Markov games for intrusion detection in cloud", J. Cases Inf. Technol, vol. 21, no. 4, October 2019, pp. 14-26.
Wen, L. “Cloud Computing Intrusion Detection Technology Based on BP-NN”, Wireless Pers Commun (2021).
Z. Chkirbene, A. Erbad, R. Hamila, A. Mohamed, M. Guizani and M. Hamdi, "TIDCS: A Dynamic Intrusion Detection and Classification System Based Feature Selection", IEEE Access, vol. 8, 2020, pp. 95864-95877.
R. Priyadharshini and E. J. Leavline, "Cuckoo optimisation based intrusion detection system for cloud computing", Int. J. Comput. Netw. Inf. Secur, vol. 10, no. 11, 2018, pp. 42-49 Nov.
Altigani, S. Hasan, B. Barry, S. Naserelden, M. A. Elsadig and H. T. Elshoush, "A Polymorphic Advanced Encryption Standard – A Novel Approach," IEEE Access, vol. 9, 2021, pp. 20191-20207.
Prabhakaran, V, Kulandasamy, A. “Hybrid semantic deep learning architecture and optimal advanced encryption standard key management scheme for secure cloud storage and intrusion detection”, Neural Comput & Applic vol. 33, 2021, pp. 14459–14479.
Sajith, P.J., Nagarajan, G. “Intrusion Detection System Using Deep Belief Network & Particle Swarm Optimization”, Wireless Pers Commun (2022).
Arulkumar, V. et al. “A performance enhancement of deepfake video detection through the use of a hybrid CNN Deep learning model”, International Journal of Electrical and Computer Engineering Systems. Vol. 14 No. 2 (2023). https://doi.org/10.32985/ijeces.14.2.6.
Modi, C., Patel, D. “A feasible approach to intrusion detection in virtual network layer of Cloud computing”, Sādhanā, vol. 43, 2018, pp. 114-121.
Wei He, Jufeng Li, Zhihe Tang, Beng Wu, Hui Luan, Chong Chen, and Huaqing Liang, "A Novel Hybrid CNN-LSTM Scheme for Nitrogen Oxide Emission Prediction in FCC Unit", Mathematical Problems in Engineering, Vol. 20, 17 August 2020.
Aruna, M, "Medical Healthcare System with Hybrid Block based Predictive models for Quality preserving in Medical Images using Machine Learning Techniques." 2022 International Conference on Advanced Computing Technologies and Applications (ICACTA). IEEE, 2022.
Rajakumar Boothalingam, "Optimization using lion algorithm: a biological inspiration from lion's social behaviour", Evolutionary Intelligence, Vol. 11, 2018, pp. 31–52.
Yi Ding, and Xian Fu, "Kernel-Based Fuzzy C-Means Clustering Algorithm Based on Genetic Algorithm", Neurocomputing, Vol. 188, 5 May 2016, pp. 233-238.
Seyedali Mirjalili, Seyed Mohammad Mirjalili and Andrew Lewisa, "Grey Wolf Optimizer", Advances in Engineering Software, Vol. 69, March 2014, pp 46-61.
Rimma Padovano, “Critical Analysis of Parallel and Distributed Computing and Future Research Direction of Cloud Computing”, Journal of Computing and Natural Science, vol.1, no.4, pp. 114-120, October 2021. doi: 10.53759/181X/JCNS202101017.
Nobile, M.S; Cazzaniga, P.; Besozzi, D.; Colombo, R.; Mauri, G.; Pasi, G., "Fuzzy Self-Tuning PSO: a settings-free algorithm for global optimization", Swarm and Evolutionary Computation, Vol.39, pp. 70–85. 2018
Ratnadewi, Roy Pramono Adhie, Yonatan Hutama, A. Saleh Ahmar and M I Setiawan, "Implementation Cryptography Data Encryption Standard (DES) and Triple Data Encryption Standard (3DES) Method in Communication System Based Near Field Communication (NFC)”, Journal of Physics: Conference Series, Vol. 954.2018.
F. Thabit, O. Can, S. Alhomdy, G. H. Al-Gaphari, and S. Jagtap, “A Novel Effective Lightweight Homomorphic Cryptographic Algorithm for data security in cloud computing,” International Journal of Intelligent Networks, vol. 3, pp. 16–30, 2022, doi: 10.1016/j.ijin.2022.04.001.
Acknowledgements
The authors would like to thank to the reviewers for nice comments on the manuscript.
Funding
No funding was received to assist with the preparation of this manuscript.
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of data and materials
No data available for above study.
Author information
Contributions
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Corresponding author
T Thilagam
T Thilagam
VelTech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology, TamilNadu, India.
Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this article
T Thilagam and R Aruna, “LM-GA: A Novel IDS with AES and Machine Learning Architecture for Enhanced Cloud Storage Security”, Journal of Machine and Computing, pp. 069-079, April 2023. doi: 10.53759/7669/jmc202303008.
