Enterprise-Centric Attack Path Discovery Through Machine Learning and Behavioral Analytics in Hybrid Cloud Infrastructure

Authors

  • Bassel Soudan Department of Electrical and Computer Engineering, University of Sharjah, United Arab Emirates Author

DOI:

https://doi.org/10.64235/v67tfb83

Keywords:

Hybrid Cloud Security, Attack Path Discovery, Machine Learning, Behavioral Analytics, Enterprise Cybersecurity, Graph-Based Threat Analysis, Anomaly Detection, Privilege Escalation, Lateral Movement, Security Risk Assessment.

Abstract

The increasing adoption of hybrid cloud infrastructure has introduced complex security challenges for enterprises managing interconnected on-premises systems, private clouds, and public cloud environments. Traditional security mechanisms often struggle to identify sophisticated attack paths because of distributed identities, interconnected workloads, misconfigured permissions, and continuously changing network relationships. This research proposes an enterprise-centric attack path discovery framework that integrates machine learning, behavioral analytics, and graph-based security intelligence to identify potential attack routes across hybrid cloud environments. The proposed framework collects security information from identity management systems, network traffic, cloud configurations, endpoint activities, and application logs to construct a unified representation of enterprise infrastructure. Machine learning algorithms analyze historical and real-time security events to identify abnormal access patterns, suspicious privilege escalation, lateral movement, and unusual communication behavior. Graph-based analytics establish relationships between users, devices, applications, workloads, and infrastructure resources to discover potential attack sequences and vulnerable connections. Behavioral analytics continuously evaluate deviations from established activity patterns, enabling early identification of emerging threats. Furthermore, risk-scoring mechanisms prioritize attack paths according to vulnerability severity, accessibility, privilege exposure, and potential business impact. The proposed approach aims to improve threat visibility, strengthen security monitoring, and support proactive risk mitigation. By integrating intelligent analytics with continuous security assessment, the framework provides enterprises with an adaptive approach to discovering and managing attack paths across dynamic hybrid cloud infrastructure.

References

Nunnaguppala, L. S. C., Sayyaparaju, K. K., & Padamati, J. R. (2021). Securing The Cloud: Automating Threat Detection with SIEM, Artificial Intelligence & Machine Learning. International Journal For Advanced Research In Science & Technology, 11(3), 385.

Tarigoppula, S. (2021). Policy-driven autonomous infrastructure provisioning: A framework for enterprise cloud governance using infrastructure as code. International Journal of Humanities and Information Technology, Special Issue 1, 131–141.

Ponugoti, M. (2025). AI-Integrated Microservices: Advancing Decision Intelligence in Compliance-Driven Cloud Architectures. Journal Of Engineering And Computer Sciences, 4(7), 1232-1240.

Nunnaguppala, L. S. C., Sayyaparaju, K. K., & Padamati, J. R. (2021). Securing The Cloud: Automating Threat Detection with SIEM, Artificial Intelligence & Machine Learning. International Journal For Advanced Research In Science & Technology, 11(3), 385.

Suddala, V. R. A. K. (2024). Machine learning for operational excellence: Real-world applications. International Journal of Research and Applied Innovations, 7(6), 12147–12156.

Ambalakannu, M. (2024). The emergence of AI-powered data analytics revolutionizing business intelligence. International Journal of Research and Applied Innovations, 7(6), 12157–12165.

Ambati, K. C. (2024). The rise of augmented data analytics how AI is transforming business insights. International Journal of Computer Technology and Electronics Communication, 7(6), 10068–10076.

Kavuru, R. R., & Balaji, R. (2023). Extending the service mesh into a compliance enforcement fabric: A policy-aware architecture for regulated financial platforms. International Journal of Engineering & Extended Technologies Research, 5(3), 6638–6644.

Indurthy, V. S. K. (2025). Phased Migration Strategies for Modernizing Enterprise Data Warehouses. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 8(3), 12170-12178.

Shaik, N. (2024). AI-driven scheduling for multi-cloud Kubernetes: Lessons from enterprise deployment. International Journal of Research and Applied Innovations, 7(3), 10880–10891.

Randhi, K. S. (2024). A scalable integration framework for automated utility revenue leakage detection and remediation. International Journal of Research and Applied Innovations, 7(2), 10536–10555.

Neela, S. (2022). Toward Intelligent Enterprise Integration: Cloud-Native Middleware Design Patterns and Adaptive Stream Orchestration Architectures for Autonomous Real-Time Decisioning. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(2), 154-164.

Kopparthi, G. S. (2023). Trustworthy artificial intelligence for high-stakes decision-making: Explainability, human oversight, and system reliability. International Journal of Engineering & Extended Technologies Research, 5(4), 6979–6992.

Natta, P. K. (2022). Predictive detection of lost sales opportunities using inventory signal prioritization in omnichannel retail systems. International Journal of Computer Technology and Electronics Communication, 5(4), 5483–5495.

Gantikota, S. (2023). Integrating SonarQube and IBM AppScan into Enterprise CI/CD Pipelines: A Vulnerability Mitigation Framework Achieving Over Eighty Percent Risk Reduction. International Journal of Emerging Trends in Computer Science and Information Technology, 4(3), 240-244.

Ramasamy, M. (2024). Telemetry-driven network operations: Architectures for analytics and automated remediation. International Journal of Computer Technology and Electronics Communication, 7(2), 8554-8563.

Bheemisetty, N. (2025). Transforming static server allocation into an adaptive compute for enhanced throughput and SLA compliance. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 8(3), 12187-12196.

Vemireddy, S. (2025). Observability and resilience engineering for mission-critical tax processing systems. International Journal of Engineering & Extended Technologies Research, 7(4), 10482–10486.

Aragani, V. M., Maroju, P. K., & Mudunuri, L. N. R. (2024, December). Implementing Non-functional Production Regression Testing with Kubernetes. In International Conference on Information and Communication Technology for Competitive Strategies (pp. 231-244). Singapore: Springer Nature Singapore.

Yadavalli, V. R. (2023). Cutover intelligence mesh: A dependency-aware architecture for enterprise cloud ERP migration. International Journal of Computer Technology and Electronics Communication, 6(4), 7400–7416.

Karrothu, A. (2024). Automated risk-based security controls for vulnerability detection and prevention across cloud-native software development lifecycles. International Journal of Humanities and Information Technology, 6(03), 147–166.

Manda, P. (2024). Oracle E-Business Suite modernization: The transition from 12.1. 3 to 12.2. 8. International Journal of Research and Applied Innovations, 7(3), 10838-10842.

Valavala, S. K. (2023). Applying zero trust to financial microservices through default deny authorization. International Journal of Engineering & Extended Technologies Research, 5(3), 6627–6637.

Kalakoti, M. K. R. (2022). Dependency-aware migration and resilience validation of stateful applications to enterprise cloud. International Journal of Research Publications in Engineering, Technology and Management, 5(2), 6582–6590.

Abd-Rouf, M. S. K., Adigun, P. O., Alalade, E. O., Oyekanmi, T. T., Faniyi, A. J., Oladapo, B., Awopejo, T. E., Adegoke, O. S., Jamiu, A., Michael, O. B., Obisesan, A., Ajala, S., Adekanye, M. A., Yambali, P. M., & Abd-Rouf, A. B. (2024). From molecular profiling to predictive algorithms: A conceptual machine-learning framework for mechanism-informed therapy selection in multidrug-resistant cancer. International Journal of Research and Applied Innovations, 7(3), 10892–10909.

Basavala, S. R. (2022). Security assessment framework for microservices-based applications using automated penetration testing. International Journal of Humanities and Information Technology, 4(1–3), 327–341.

Bellundagi, M. (2025). Digital transformation framework for smart enterprises using AI and cloud computing. The Eastasouth Journal of Information System and Computer Science, 3(2), 366–378. https://doi.org/10.58812/esiscs.v3i02.1299

Gowda, M. K. S. (2024). Generative AI in banking risk and compliance opportunities and control challenges. International Journal of Engineering & Extended Technologies Research, 6(6), 9421–9431.

Ahuja, D. (2025, August). Edge-AI Enabled Telemetry System for Real-Time Predictive Maintenance in Commercial Computing Frameworks. In 2025 Global Conference on Information Technology and Communication Networks (GITCON) (pp. 1-7). IEEE.

Kommineni, R. (2024). Normalize the inputs, not the clouds: Schema-validated profile generation and collision-free address planning for multi-cloud infrastructure as code. International Journal of Emerging Trends in Engineering and Management Research, 9(4), 16116–16123.

Mudunuri, P. R. (2024). Designing high-availability automation architectures for mission-critical research systems. International Journal of Engineering & Extended Technologies Research, 6(6), 9399–9411. https://doi.org/10.15662/IJEETR.2024.0606038

Selvarajan, K. (2023). Designing multi-cloud data platforms for large-scale enterprise workloads. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(1), 5992-6002.

Panda, S. S. (2025). Breaking dependency chains: Evaluating Microsoft’s Maia 100 as an alternative to NVIDIA GPUs in AI workloads. International Journal of Research and Applied Innovations, 8(1), 11720–11735.

Garg, D. K. (2025). Manufacturing execution systems: A comprehensive guide to selection and implementation. International Journal of Research in Computer Applications and Information Technology, 8(1), 2001–2015. https://doi.org/10.34218/IJRCAIT_08_01_146

Reddy, K. V. (2021). UVM methodology for industry-specific applications using AI in hardware verification. International Journal of Engineering & Extended Technologies Research, 3(3), 3094–3107.

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Published

2026-11-05

How to Cite

Enterprise-Centric Attack Path Discovery Through Machine Learning and Behavioral Analytics in Hybrid Cloud Infrastructure. (2026). Journal of Cyber-Physical Security and Robotics, 1(02), 257-265. https://doi.org/10.64235/v67tfb83

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