An Enhanced Security Framework for Storage using PSO in Cloud Computing

Authors

  • Dinesh Parkash M.M. Institute of Computer Technology and Business Management, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, Haryana 133203, India
  • Sumit Mittal M.M. Institute of Computer Technology and Business Management, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, Haryana 133203, India

Keywords:

Naïve Bayes (NB), Particle Swarm Optimization (PSO), Artificial Bee Colony Algorithm (ABC), KDD-99, NSL-KDD dataset, Cuckoo Search Algorithm (CSA)

Abstract

The expansion in web usage demands security issues with it. Poor programming can influence the activity of the frameworks and information secrecy because of the security holes in the frameworks. Intrusion Detection System (IDS) has been developed to identify and report assaults. To encourage IDS frameworks, Artificial Intelligence based approaches have been utilized. The development in the field of cloud computing has provided a multifunctional view for the clients such as normalized applications to clients on the web that can be maintained on a routine basis. Cloud Computing is utilized for information stockpiling, so that information security and protection issues such as Confidentiality, Availability, and Integrity should be met. The universality of cloud computing allows individuals to store their information on the web. In our research idea, first of all we have designed an  IDS (Intrusion Detection System) for Cloud Security using Artificial Bee Colony Algorithm (ABC) and then we have designed an enhanced framework for cloud storage with a secure environment using Particle Swarm Optimization Algorithm (PSO). Firstly, we considered three performance measures like recall, accuracy, and precision, and then to improve the efficiency of the proposed model, we have added two new metrics like F-measure and confusion matrix. After that we have compared this proposed model with the existing models. In this research proposal, the developed model trained with the NSL-KDD dataset is being presented.   

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References

Abuhussein, A., Bedi, H., & Shiva, S. (2013). Towards a Stakeholder-Oriented Taxonomical Approach for Secure Cloud Computing. 2013 IEEE Sixth International Conference on Cloud Computing. doi:10.1109/cloud.2013.132.

De los Reyes, G., Macwan, S., Chawla, D., & Serban, C. (2012). Securing the mobile enterprise with network-based security and cloud computing. 2012 35th IEEE Sarnoff Symposium. doi:10.1109/sarnof.2012.6222759.

Markandey, A., Dhamdhere, P., &Gajmal, Y. (2018). Data Access Security in Cloud Computing: A Review. 2018 International Conference on Computing, Power and Communication Technologies (GUCON). doi:10.1109/gucon.2018.8675033

Hendre, A., & Joshi, K. P. (2015). A Semantic Approach to Cloud Security and Compliance. 2015 IEEE 8th International Conference on Cloud Computing. doi:10.1109/cloud.2015.157

Abuhussein, A., Bedi, H., & Shiva, S. (2013). Towards a Stakeholder-Oriented Taxonomical Approach for Secure Cloud Computing. 2013 IEEE Sixth International Conference on Cloud Computing. doi:10.1109/cloud.2013.132.

Parkash D. ,Mittal S.(2020) Comparative study and performance analysis of various data security and cloud storage models. J Solid State Technology, 63(2s), 6318–6331.

Parkash D.,Mittal S.(2022) An Enhanced Secure Framework Using CSA for Cloud Computing Environments.International Conference on Innovative Computing and Communications, Proceedings of ICICC 2022,Volume 2(349-356). Lecture Notes in Networks and Systems (471) by Springer(https://link.springer.com/chapter/10.1007/978-981-19-2535-1_27).

Parkash D.,Mittal S.(2022) An Efficient Security Framework Using ABC in Cloud Computing.2nd International Conference on Research Trends in Engineering and Management, Proceedings of ICRTEM- 2022,(60-64).

Hajimirzaei, B., & Navimipour, N. J. (2018). Intrusion detection for cloud computing using neural networks and artificial bee colony optimization algorithm. ICT Express. doi:10.1016/j.icte.2018.01.014.

Salem, R., Salam, M. A., Abdelkader, H., awad, A., & Arafa, A. (2019). An Artificial Bee Colony Algorithm for Data Replication Optimization in Cloud Environments. IEEE Access, 1–1. doi:10.1109/access.2019.2957436.

Nil C., Patriciu V. V. and Bica I., “MACHINE LEARNING_DATASETS FOR CYBER SECURIT_APPLICATIONS” Vol. 3 (2019), Issue 3, pg(s) 109-112

Thomas C., V. Sharma, N. Balakrishnan, “Usefulness of DARPA dataset for intrusion detection system evaluation” in Proceedings of SPIE - The International Society for Optical Engineering, March 2008.

Fu, X., Liu, W., Cang, Y., Gong, X., & Deng, S. (2016). Optimized Data Replication for Small Files in Cloud Storage Systems. Mathematical Problems in Engineering, 2016, 1–8. doi:10.1155/2016/4837894.

Kumar, A., Lee, B. G., Lee, H., & Kumari, A. (2012). Secure storage and access of data in cloud computing. 2012 International Conference on ICT Convergence (ICTC). doi:10.1109/ictc.2012.6386854.

Ingre, B., & Yadav, A. (2015). Performance analysis of NSL-KDD dataset using ANN. 2015 International Conference on Signal Processing and Communication Engineering Systems. doi:10.1109/spaces.2015.7058223.

Tavallaee, M., Stakhanova, N., & Ghorbani, A. A. (2010). Toward Credible Evaluation of Anomaly-Based Intrusion-Detection Methods. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(5), 516–524. doi:10.1109/tsmcc.2010.2048428.

Roempluk, T., & Surinta, O. (2019). A Machine Learning Approach for Detecting Distributed Denial of Service Attacks. 2019 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT-NCON). doi:10.1109/ecti-ncon.2019.8692243.

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Published

24.11.2023

How to Cite

Parkash, D. ., & Mittal, S. . (2023). An Enhanced Security Framework for Storage using PSO in Cloud Computing. International Journal of Intelligent Systems and Applications in Engineering, 12(5s), 513–520. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3960

Issue

Vol. 12 No. 5s (2024)

Section

Research Article

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