Cybersecurity Adversarial Threats and IDS in Cloud-Enabled Enterprises From Risk Prediction to Autonomous Defense
DOI:
https://doi.org/10.15662/IJEETR.2025.0704006Keywords:
Cybersecurity, Cloud Computing, Adversarial Threats, Intrusion Detection System (IDS), Predictive Analytics, Autonomous Defense, Enterprise Security, Threat MitigationAbstract
Cloud-enabled enterprises face increasingly sophisticated cybersecurity threats, including adversarial attacks that target network vulnerabilities, cloud workloads, and critical enterprise data. Intrusion Detection Systems (IDS) play a central role in identifying, predicting, and mitigating these threats. This study explores the integration of predictive analytics, machine learning, and autonomous response mechanisms in IDS architectures for cloud environments. By analyzing attack patterns and simulating adversarial scenarios, the research proposes a framework for enhancing threat detection accuracy, reducing response time, and improving overall enterprise security posture. Results demonstrate the effectiveness of predictive IDS in mitigating cyber risks while maintaining operational efficiency and scalability.
References
1. Bates, D. W. (2018). Big data in healthcare. Health Affairs, 37(7), 1130–1137.
2. Udayakumar, R., Joshi, A., Boomiga, S. S., & Sugumar, R. (2023). Deep fraud Net: A deep learning approach for cyber security and financial fraud detection and classification. Journal of Internet Services and Information Security, 13(3), 138-157.
3. Mahajan, N. (2023). A predictive framework for adaptive resources allocation and risk-adjusted performance in engineering programs. Int. J. Intell. Syst. Appl. Eng, 11(11s), 866.
4. Singh, A. (2025). Intent-Based Networking in Multi-Cloud Environments. Journal of Engineering and Applied Sciences Technology, 7(2), 1-7.
5. Kiran, A., Rubini, P., & Kumar, S. S. (2025). Comprehensive review of privacy, utility and fairness offered by synthetic data. IEEE Access.
6. Poornima, G., & Anand, L. (2024, May). Novel AI Multimodal Approach for Combating Against Pulmonary Carcinoma. In 2024 5th International Conference for Emerging Technology (INCET) (pp. 1-6). IEEE.
7. Adari, Vijay Kumar, “Interoperability and Data Modernization: Building a Connected Banking Ecosystem,” International Journal of Computer Engineering and Technology (IJCET), vol. 15, no. 6, pp.653-662, Nov-Dec 2024. DOI:https://doi.org/10.5281/zenodo.14219429.
8. Sivaraju, P. S. (2024). Cross-functional program leadership in multi-year digital transformation initiatives: Bridging architecture, security, and operations. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(6), 11374-11380.
9. Karnam, A. (2024). Engineering Trust at Scale: How Proactive Governance and Operational Health Reviews Achieved Zero Service Credits for Mission-Critical SAP Customers. International Journal of Humanities and Information Technology, 6(4), 60–67. https://doi.org/10.21590/ijhit.06.04.11
10. Mani, R. (2024). Smart Resource Management in SAP HANA: A Comprehensive Guide to Workload Classes, Admission Control, and System Optimization through Memory, CPU, and Request Handling Limits. International Journal of Research and Applied Innovations, 7(5), 11388-11398.
11. Rodrigues, G. N., Mir, M. N. H., Bhuiyan, M. S. M., Rafi, M. D. A. L., Hoque, A. M., Maua, J., & Mridha, M. F. (2025). NLP-driven customer segmentation: A comprehensive review of methods and applications in personalized marketing. Data Science and Management.
12. Joyce, S., Pasumarthi, A., & Anbalagan, B. (2025). SECURITY OF SAP SYSTEMS IN AZURE: ENHANCING SECURITY POSTURE OF SAP WORKLOADS ON AZURE–A COMPREHENSIVE REVIEW OF AZURENATIVE TOOLS AND PRACTICES.||.
13. Hollis, M., Omisola, J. O., Patterson, J., Vengathattil, S., & Papadopoulos, G. A. (2020). Dynamic Resilience Scoring in Supply Chain Management using Predictive Analytics. The Artificial Intelligence Journal, 1(3).
14. Chivukula, V. (2024). The Role of Adstock and Saturation Curves in Marketing Mix Models: Implications for Accuracy and Decision-Making.. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(2), 10002–10007.
15. Natta P K. AI-Driven Decision Intelligence: Optimizing Enterprise Strategy with AI-Augmented Insights[J]. Journal of Computer Science and Technology Studies, 2025, 7(2): 146-152.
16. Kusumba, S. (2023). Achieving Financial Certainty: A Unified Ledger Integrity System for Automated, End-to-End Reconciliation. The Eastasouth Journal of Information System and Computer Science, 1(01), 132-143.
17. Navandar, P. (2022). SMART: Security Model Adversarial Risk-based Tool. International Journal of Research and Applied Innovations, 5(2), 6741-6752.
18. Md Al Rafi. (2022). Intelligent Customer Segmentation: A Data- Driven Framework for Targeted Advertising and Digital Marketing Analytics. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(5), 7417–7428.
19. Vasugi, T. (2022). AI-Optimized Multi-Cloud Resource Management Architecture for Secure Banking and Network Environments. International Journal of Research and Applied Innovations, 5(4), 7368-7376.
20. Achari, A. P. S. K., & Sugumar, R. (2024, November). Performance analysis and determination of accuracy using machine learning techniques for naive bayes and random forest. In AIP Conference Proceedings (Vol. 3193, No. 1, p. 020199). AIP Publishing LLC.
21. Sakinala, K. (2025). Monitoring and observability for cloud-native applications. Journal of Computer Science and Technology Studies, 7(8), 101-115.
22. Paul, D., Sudharsanam, S. R., & Surampudi, Y. (2021). Implementing Continuous Integration and Continuous Deployment Pipelines in Hybrid Cloud Environments: Challenges and Solutions. Journal of Science & Technology, 2(1), 275-318.
23. Nagarajan, G. (2024). Cloud-Integrated AI Models for Enhanced Financial Compliance and Audit Automation in SAP with Secure Firewall Protection. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(1), 9692-9699.
24. Rajurkar, P. AI-Driven Fenceline Monitoring for Real-Time Detection of Hazardous Air Pollutants in Industrial Corridors. (Tjosvold, 1998)
25. Kanumarlapudi, P. K., Peram, S. R., & Kakulavaram, S. R. (2024). Evaluating Cyber Security Solutions through the GRA Approach: A Comparative Study of Antivirus Applications. International Journal of Computer Engineering and Technology (IJCET), 15(4), 1021-1040.
26. Madabathula, L. (2025). Dynamic Data Orchestration: Enhancing Business Intelligence with Azure Data Factory. IJSAT-International Journal on Science and Technology, 16(1).
27. Meka, S. (2024). Securing Instant Payments: Implementing Fraud Prevention Frameworks with AVS and OTP Validation. Journal Code, 1763, 4821.
28. Balaji, K. V., & Sugumar, R. (2023, December). Harnessing the Power of Machine Learning for Diabetes Risk Assessment: A Promising Approach. In 2023 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI) (pp. 1-6). IEEE.
29. Chukkala, R. (2025, April). The Convergence of CCAI, Chatbots, and RCS Messaging: Redefining Business Communication in the AI Era. In International Conference of Global Innovations and Solutions (pp. 194-213). Cham: Springer Nature Switzerland.
30. Adejumo, E. O. Cross-Sector AI Applications: Comparing the Impact of Predictive Analytics in Housing, Marketing, and Organizational Transformation. https://www.researchgate.net/profile/Ebunoluwa-Adejumo/publication/396293578_Cross-Sector_AI_Applications_Comparing_the_Impact_of_Predictive_Analytics_in_Housing_Marketing_and_Organizational_Transformation/links/68e5fdcae7f5f867e6ddd573/Cross-Sector-AI-Applications-Comparing-the-Impact-of-Predictive-Analytics-in-Housing-Marketing-and-Organizational-Transformation.pdf
31. Harish, M., & Selvaraj, S. K. (2023, August). Designing efficient streaming-data processing for intrusion avoidance and detection engines using entity selection and entity attribute approach. In AIP Conference Proceedings (Vol. 2790, No. 1, p. 020021). AIP Publishing LLC.
