Articles
AI-Augmented Cloud Security Systems for Advanced Threat Detection in Enterprises
Abstract
The rapid adoption of multi-cloud architectures by enterprises coupled with the growing sophistication of cybercriminals necessitates new approaches to cloud security. Cloud service providers have made great strides in guarantee controls, but the attack surfaces of cloud-hosted enterprise applications remain the responsibility of the tenants. AI can help identify when a cloud-hosted enterprise application experiences abnormal behavior that indicates the presence of an advanced persistent threat (APT) or other malicious action, such as data exfiltration or unauthorized access.
Research publications on the use of AI-augmented cloud security systems capable of accurately identifying malicious actions within enterprise cloud-hosted applications are growing. A survey of existing AI-augmented cloud security implementations and the techniques used to protect these enterprise applications contribute to a set of guidelines for enhancing the detection of advanced persistent threats and other malicious activity that simply evades current signature-based detection systems.
Article Information
Journal |
International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
|---|---|
Volume (Issue) |
Vol. 6 No. 6 (2023): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
DOI |
|
Pages |
12871-12886 |
Published |
December 10, 2023 |
| Copyright | |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Dhanaraj Sathiri (2023). AI-Augmented Cloud Security Systems for Advanced Threat Detection in Enterprises. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 6 No. 6 (2023): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 12871-12886. https://doi.org/10.15662/IJAESIT.2023.0606006 |
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20. Uday Surendra Yandamuri. (2023). An Intelligent Analytics Framework Combining Big Data and Machine Learning for Business Forecasting. International Journal Of Finance, 36(6), 682-706. https://doi.org/10.5281/zenodo.18095256
21. Aljawarneh, S., Aldwairi, M., & Yassein, M. B. (2018). Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model. Journal of Computational Science, 25, 152–160.
22. Li, Y., Chen, C. Y., Wasserman, W. W., & Ramani, A. K. (2016). Deep feature selection. Bioinformatics, 32(5), 743–750.
23. Nandan, B. P., & Chitta, S. S. (2023). Machine Learning Driven Metrology and Defect Detection in Extreme Ultraviolet (EUV) Lithography: A Paradigm Shift in Semiconductor Manufacturing. Educational Administration: Theory and Practice, 29 (4), 4555–4568.
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27. Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018). Deep learning for healthcare. Briefings in Bioinformatics, 19(6), 1236–1246.
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30. Guntupalli, R. (2023). Optimizing Cloud Infrastructure Performance Using AI: Intelligent Resource Allocation and Predictive Maintenance. Available at SSRN 5329154.
31. Patcha, A., & Park, J. M. (2007). An overview of anomaly detection techniques. Computer Networks, 51(12), 3448–3470.
32. Pedregosa, F., Varoquaux, G., Gramfort, A., et al. (2011). Scikit-learn. Journal of Machine Learning Research, 12, 2825–2830.
33. Aitha, A. R. (2023). CloudBased Microservices Architecture for Seamless Insurance Policy Administration. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 607-632.
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