Leveraging Artificial Intelligence for Advanced Cloud Security: Discussing Techniques and Applications

Basheer Ullah; Arif  Kamal; Saad Ali Asif

doi:10.52633/4yjkrf74

Authors

Basheer Ullah Khadim Ali Shah Bukhari Institute of Technology, Karachi, Pakistan Author
Arif Kamal Khadim Ali Shah Bukhari Institute of Technology, Karachi, Pakistan. Author
Saad Ali Asif Khadim Ali Shah Bukhari Institute of Technology, Karachi, Pakistan. Author

DOI:

https://doi.org/10.52633/4yjkrf74

Keywords:

Cyber Security, Cloud Computing, AI Integration, Risk Management, Machine Learning, Anomaly Detection

Abstract

Cloud computing has revolutionized information technology by providing highly scalable, flexible, and cost-effective services. At the same time, it has opened new severe challenges for security, requiring advanced security paradigms. In this research paper, the integration of Artificial Intelligence in cloud security is explored, exposing its potential to provide proactivity with the detection of threats, real-time incident response, and comprehensive risk management. It elaborates on key AI advancements: machine learning, anomaly detection, predictive analytics, and automated threat response. The paper has also tried to probe further into the basic building blocks of cloud architecture, bringing forth the emerging threats and analyzing how AI techniques can help strengthen cloud infrastructures against sophisticated cyber threats. Practical benefits and effectiveness of AI-powered cloud security solutions can be demonstrated via real-world case studies from leading vendors such as Microsoft Azure, IBM, Google Cloud, Amazon Web Services, and BlackBerry. The paper concludes by looking into the future AI-driven cloud security trends, which already emphasize: Proactive threat detection. Adaptive security frameworks. Privacy-preserving AI techniques. This research aims to shed light on the interface between AI and cloud security for academia and industry, where invaluable insights will be proffered, together with the needed direction for researchers, practitioners, and decision-makers.

Author Biographies

Basheer Ullah, Khadim Ali Shah Bukhari Institute of Technology, Karachi, Pakistan

Department of Computer Science and Information Systems, Khadim Ali Shah Bukhari Institute of Technology, Karachi, Pakistan.
Arif Kamal, Khadim Ali Shah Bukhari Institute of Technology, Karachi, Pakistan.

Department of Computer Science and Information Systems, Khadim Ali Shah Bukhari Institute of Technology, Karachi, Pakistan.
Saad Ali Asif, Khadim Ali Shah Bukhari Institute of Technology, Karachi, Pakistan.

Department of Computer Science and Information Systems, Khadim Ali Shah Bukhari Institute of Technology, Karachi, Pakistan.

References

Ahmed, M., Mahmood, A. N., & Hu, J. (2016). A survey of network anomaly detection techniques. Journal of Network and Computer Applications, 60, 19-31.

Alliance, C. S. (2019). Top threats to cloud computing: Egregious eleven,”. CSA Report.

Alosaimi, W., Zak, M., & Al-Begain, K. (2015, September). Denial of service attacks mitigation in the cloud. In 2015 9th International Conference on Next Generation Mobile Applications, Services and Technologies (pp. 47-53). IEEE.

Amazon Web Services. (2021). *AWS GuardDuty: Intelligent Threat Detection*. Retrieved from [AWS](https://aws.amazon.com/guardduty/)

BlackBerry. (2020). AI-driven Security: Cylance in Healthcare. Retrieved from [BlackBerry](https://www.blackberry.com/us/en/cylance)

Buyya, R., Vecchiola, C., & Selvi, S. T. (2013). Mastering cloud computing: foundations and applications programming. McGraw-Hill.

Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys (CSUR), 41(3), 1-58.

Duc, B. M., & Cuong, V. H. (2022). A Systematic Analysis of Cloud Security Challenges and Mitigation Strategies in Modern Organizations. International Journal of Social Analytics, 7(12), 11-25.

Eberle, W., Holder, L., & Cook, D. (2009). Identifying threats using graph-based anomaly detection. In Machine Learning for Cyber Trust: Security, Privacy, and Reliability (pp. 73-108). Boston, MA: Springer US.

Erl, T., Puttini, R., & Mahmood, Z. (2013). Cloud computing: concepts, technology & architecture. Pearson Education.

Gioe, D. V., & Hatfield, J. M. (2021). A damage assessment framework for insider threats to national security information: Edward Snowden and the Cambridge Five in comparative historical perspective. Cambridge Review of International Affairs, 34(5), 704-738.

Giura, P., & Wang, W. (2012). Using large scale distributed computing to unveil advanced persistent threats. Science J, 1(3), 93-105.

Google Cloud. (2022). Chronicle Security Operations: AI for Threat Detection. Retrieved from [Google Cloud] (https://cloud.google.com/chronicle)

Huang, C. Y., Tsai, Y. T., & Hsu, C. H. (2023). Performance evaluation on permission-based detection for Android malware. In Advances in Intelligent Systems and Applications-Volume 2: Proceedings of the International Computer Symposium ICS 2012 Held at Hualien, Taiwan, December 12–14, 2012 (pp. 111-120). Springer Berlin Heidelberg.

IBM. (2021). Case Study: Enhancing Security in the Banking Sector with Cloud Pak. Retrieved from [IBM Cloud] (https://www.ibm.com/cloud/cloud-pak-for-security)

Jensen, M., Schwenk, J., Gruschka, N., & Iacono, L. L. (2009, September). On technical security issues in cloud computing. In 2009 IEEE international conference on cloud computing (pp. 109-116). IEEE.

Kasongo, S. M. (2023). A deep learning technique for intrusion detection system using a Recurrent Neural Networks based framework. Computer Communications, 199, 113-125.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.

Liu, Y., Sun, Y. L., Ryoo, J., & Vasilakos, A. V. (2015). A survey of security and privacy challenges in cloud computing: solutions and future directions.

Mell, P. (2011). NIST Definition of Cloud Computing. Recommendations of the National Institute of Standards and Technology.

Microsoft. (2020). Azure Sentinel: Scalable security with AI. Retrieved from [Microsoft Azure] (https://azure.microsoft.com/en-us/services/azure-sentinel/)

Mishra, A., Gupta, N., & Gupta, B. B. (2020). Security threats and recent countermeasures in cloud computing. In Modern principles, practices, and algorithms for cloud security (pp. 145-161). IGI Global.

Mohaisen, A., Alrawi, O., & Mohaisen, M. (2015). AMAL: high-fidelity, behavior-based automated malware analysis and classification. Computers & Security, 52, 251-266.

Nguyen, T. T., Pathan, A.-S. K., & Bui, X.-N. (2020). A deep learning model for network intrusion detection utilizing convolutional and recurrent neural networks. IEEE Access, 8, 85040-85052.

Oltsik, J. (2017). The emergence of security operations and analytics platform architecture (SOAPA). Enterprise Strategy Group, 2017.

Ramaswamy, Y., & Sankaran, V. N. (2024). Advanced Cybersecurity Strategies in Cloud Computing: Techniques for Data Protection and Privacy. Library Progress International, 44(3), 2643-2656.

Rittinghouse, J. W., & Ransome, J. F. (2017). Cloud computing: implementation, management, and security. CRC Press.

Shokri, R., & Shmatikov, V. (2015, October). Privacy-preserving deep learning. In Proceedings of the 22nd ACM SIGSAC conference on computer and communications security (pp. 1310-1321).

Sommer, R., & Paxson, V. (2010, May). Outside the closed world: On using machine learning for network intrusion detection. In 2010 IEEE symposium on security and privacy (pp. 305-316). IEEE.

Sosinsky, B. (2010). Cloud computing bible. John Wiley & Sons.

Sperotto, A., & Pras, A. (2011, May). Flow-based intrusion detection. In 12th IFIP/IEEE International Symposium on Integrated Network Management (IM 2011) and Workshops (pp. 958-963). IEEE.

Vinayakumar, R., Soman, K. P., & Poornachandran, P. (2018). Evaluating deep learning approaches to characterize and classify malicious URLs. Journal of Intelligent & Fuzzy Systems, 34(3), 1333-1343.

Wolff, E. D., GroWlEy, K. M., Lerner, M. O., Welling, M. B., Gruden, M. G., & Canter, J. (2021). Navigating the SolarWinds supply chain attack. Procurement Law., 56, 3.

Yamany, H. F. E., Capretz, M. A., & Allison, D. S. (2010). Intelligent security and access control framework for service-oriented architecture. Information and Software Technology, 52(2), 220-236.