Data Leakage Detection in Cloud Computing Environment Using Classification Based on Deep Learning Architectures

Authors

  • Rajashekhargouda C. Patil Associate Professor, Electronics and Communication Engineering Visvesvaraya Technological University
  • Ajay Kumar Assistant Professor, Bharati Vidyapeeth (Deemed to be University) Institute of Management and Research, New Delhi, India
  • Narmadha T. Assistant Professor, Department of Computer Science and Engineering Jain(Deemed-to-be University), Bangalore, India
  • M. Suganthi Assistant Professor, Computer Science and Engineering, Thamirabharani Engineering College, Tamil Nadu/India
  • Akula VS Siva Rama Rao Associate Professor, Dept of CSE, Sasi Institute of Technology & Engineering, Tadepalligudem
  • Rajesh A. Professor, Department of CSE, Faculty of Engineering and Technology JAIN (Deemed-to-be University), Karnataka,

Keywords:

cybersecurity, data leakage detection, cloud computing, data classification, deep learning

Abstract

Insider threats are hostile actions that a legitimate employee of a company could commit. For both commercial and governmental enterprises, insider threats pose a significant cybersecurity risk since they have a considerably greater potential to harm an organization's assets than external attacks. The majority of currently utilised insider threat methodologies concentrated on identifying common insider attack scenarios. This research propose novel technique in data leakage detection in cloud computing based on data classification using deep learning architectures. Here the input data has been collected as network data and processed for noise removal, smoothening. The classification has been done based on Generative Regression kernel SVM. The experimental findings have been calculated in terms of RMSE, SNR, F-1 score, recall, accuracy, and precision. The proposed model offers practical approaches to deal with potential bias and class imbalance issues in order to design a system that effectively detects insider data leaking. Proposed technique attained accuracy of 97%, precision of 92%, recall of 67%, F-1 score of 66%, RMSE 62% and SNR of 61%.

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References

Okochi, P. I., Okolie, S. A., & Odii, J. N. (2021). An improved data leakage detection system in a cloud computing environment. World Journal of Advanced Research and Reviews, 11(2), 321-328.

Gupta, I., Mittal, S., Tiwari, A., Agarwal, P., & Singh, A. K. (2022). TIDF-DLPM: Term and Inverse Document Frequency based Data Leakage Prevention Model. arXiv preprint arXiv:2203.05367.

Gupta, I., & Singh, A. K. (2022). A Holistic View on Data Protection for Sharing, Communicating, and Computing Environments: Taxonomy and Future Directions. arXiv preprint arXiv:2202.11965.

Mayuranathan, M., Saravanan, S. K., Muthusenthil, B., & Samydurai, A. (2022). An efficient optimal security system for intrusion detection in cloud computing environment using hybrid deep learning technique. Advances in Engineering Software, 173, 103236.

Singh, P., & Ranga, V. (2021). Attack and intrusion detection in cloud computing using an ensemble learning approach. International Journal of Information Technology, 13(2), 565-571.

Gupta, R., Saxena, D., & Singh, A. K. (2021). Data security and privacy in cloud computing: concepts and emerging trends. arXiv preprint arXiv:2108.09508.

Chhabra, S., & Singh, A. K. (2022). A Comprehensive Vision on Cloud Computing Environment: Emerging Challenges and Future Research Directions. arXiv preprint arXiv:2207.07955.

Al-Shehari, T., & Alsowail, R. A. (2021). An insider data leakage detection using one-hot encoding, synthetic minority oversampling and machine learning techniques. Entropy, 23(10), 1258.

Alshammari, S. T., & Alsubhi, K. (2021). Building a reputation attack detector for effective trust evaluation in a cloud services environment. Applied Sciences, 11(18), 8496.

Chaudhary, H., Chaudhary, H., & Sharma, A. K. (2022). Optimized Genetic Algorithm and Extended Diffie Hellman as an Effectual Approach for DOS-Attack Detection in Cloud. International Journal of Software Engineering and Computer Systems, 8(1), 69-78.

Wang, X., Pan, Z., Zhang, J., & Huang, J. (2021). Detection and elimination of project engineering security risks from the perspective of cloud computing. International Journal of System Assurance Engineering and Management, 1-9.

Sharma, A., Singh, U. K., Upreti, K., & Yadav, D. S. (2021, October). An investigation of security risk & taxonomy of Cloud Computing environment. In 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC) (pp. 1056-1063). IEEE.

An overview of proposed system

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Published

19.12.2022

How to Cite

Rajashekhargouda C. Patil, Ajay Kumar, Narmadha T., M. Suganthi, Akula VS Siva Rama Rao, & Rajesh A. (2022). Data Leakage Detection in Cloud Computing Environment Using Classification Based on Deep Learning Architectures. International Journal of Intelligent Systems and Applications in Engineering, 10(2s), 281–285. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2401

Issue

Vol. 10 No. 2s (2022)

Section

Research Article

License

Copyright (c) 2022 Rajashekhargouda C. Patil, Ajay Kumar, Narmadha T., M. Suganthi, Akula VS Siva Rama Rao, Rajesh A.

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