Secure Identity and Access Management Model for Distributed and Federated Systems

References

[1] Afriyie, D., 2017. LEVERAGING PREDICTIVE PEOPLE ANALYTICS TO OPTIMIZE WORKFORCE MOBILITY, TALENT RETENTION, AND REGULATORY COMPLIANCE IN GLOBAL ENTERPRISES [online]

[2] Ajayi, J. O., & [Additional authors if available]. (n.d.). An expenditure monitoring model for capital project efficiency in governmental and large-scale private sector institutions. International Journal of Scientific Research in Computer Science, Engineering and Information Technology. https://doi.org/10.32628/IJSRCSEIT

[3] Ajayi, J. O., Erigha, E. D., Obuse, E., Ayanbode, N., & Cadet, E. (n.d.). Anomaly detection frameworks for early-stage threat identification in secure digital infrastructure environments. International Journal of Scientific Research in Computer Science, Engineering and Information Technology. https://doi.org/10.32628/IJSRCSEIT

[4] Alliance, N.G.M.N., 2017. 5g end-to-end architecture framework. Tech. Rep., pp.04-0ct.

[5] Anilkumar, C. and Sumathy, S., 2018. Security strategies for cloud identity management—A study. International Journal of Engineering & Technology, 7(2), pp.732-741.

[6] Annam, S.N., 2018. Emerging trends in IT management for large corporations. International Journal of Scientific Research in Science, Engineering and Technology, 4(8), p.770.

[7] Ayanbode, N., Cadet, E., Etim, E. D., Essien, I. A., & Ajayi, J. O. (2019). Deep learning approaches for malware detection in large-scale networks. IRE Journals, 3(1), 483–489. https://irejournals.com/formatedpaper/1710371.pdf

[8] Ayanbode, N., Cadet, E., Etim, E. D., Essien, I. A., & Ajayi, J. O. (n.d.). Developing AI-augmented intrusion detection systems for cloud-based financial platforms with real-time risk analysis. International Journal of Scientific Research in Computer Science, Engineering and Information Technology. https://doi.org/10.32628/IJSRCSEIT

[9] Babatunde, L. A., Cadet, E., Ajayi, J. O., Erigha, E. D., Obuse, E., Ayanbode, N., & Essien, I. A. (n.d.). Simplifying third-party risk oversight through scalable digital governance tools. International Journal of Scientific Research in Computer Science, Engineering and Information Technology. https://doi.org/10.32628/IJSRCSEIT

[10] Bankole, F. A., & Lateefat, T. (2019). Strategic cost forecasting framework for SaaS companies to improve budget accuracy and operational efficiency. IRE Journals, 2(10), 421–432.

[11] Beaty, K.A., Chow, J.M., Cunha, R.L., Das, K.K., Hulber, M.F., Kundu, A., Michelini, V. and Palmer, E.R., 2016. Managing sensitive applications in the public cloud. IBM Journal of Research and Development, 60(2-3), pp.4-1.

[12] Bhatia, T. and Verma, A.K., 2017. Data security in mobile cloud computing paradigm: a survey, taxonomy and open research issues. The Journal of Supercomputing, 73(6), pp.2558-2631.

[13] Boone, W. and McDougall, A., 2016. Governance and compliance. Handbook of SCADA/Control Systems Security, 201.

[14] Brzezina, N., Kopainsky, B. and Mathijs, E., 2016. Can organic farming reduce vulnerabilities and enhance the resilience of the European food system? A critical assessment using system dynamics structural thinking tools. Sustainability, 8(10), p.971.

[15] Buecker, A., Chakrabarty, B., Dymoke-Bradshaw, L., Goldkorn, C., Hugenbruch, B., Nali, M.R., Ramalingam, V., Thalouth, B. and Thielmann, J., 2016. Reduce Risk and Improve Security on IBM Mainframes: Volume 1 Architecture and Platform Security. IBM Redbooks.

[16] Channuntapipat, C., 2018. Assurance for service organisations: contextualising accountability and trust. Managerial Auditing Journal, 33(4), pp.340-359.

[17] Dako, O. F., Onalaja, T. A., Nwachukwu, P. S., Bankole, F. A., & Lateefat, T. (2019). Blockchain-enabled systems fostering transparent corporate governance, reducing corruption, and improving global financial accountability. IRE Journals, 3(3), 259–266.*

[18] Dako, O. F., Onalaja, T. A., Nwachukwu, P. S., Bankole, F. A., & Lateefat, T. (2019). AI-driven fraud detection enhancing financial auditing efficiency and ensuring improved organizational governance integrity. IRE Journals, 2(11), 556–563.*

[19] Dako, O. F., Onalaja, T. A., Nwachukwu, P. S., Bankole, F. A., & Lateefat, T. (2019). Business process intelligence for global enterprises: Optimizing vendor relations with analytical dashboards. IRE Journals, 2(8), 261–270.*

[20] Dalal, A., 2018. Driving Business Transformation through Scalable and Secure Cloud Computing Infrastructure Solutions. Available at SSRN 5424274.

[21] Dare, S. O., Ajayi, J. O., & Chima, O. K. (n.d.). An integrated decision-making model for improving transparency and audit quality among small and medium-sized enterprises. International Journal of Scientific Research in Computer Science, Engineering and Information Technology. https://doi.org/10.32628/IJSRCSEIT

[22] Demchenko, Y., Turkmen, F., de Laat, C., Hsu, C.H., Blanchet, C. and Loomis, C., 2017. Cloud computing infrastructure for data intensive applications. In Big Data Analytics for Sensor-Network Collected Intelligence (pp. 21-62). Academic Press.

[23] Dorgbefu, E.A., 2018. Translating complex housing data into clear messaging for real estate investors through modern business communication techniques. International Journal of Computer Applications Technology and Research, 7(12), pp.485-499.

[24] Essien, I. A., Ajayi, J. O., Erigha, E. D., Obuse, E., & Ayanbode, N. (n.d.). Supply chain fraud risk mitigation using federated AI models for continuous transaction integrity verification. International Journal of Scientific Research in Computer Science, Engineering and Information Technology. https://doi.org/10.32628/IJSRCSEIT

[25] Essien, I. A., Cadet, E., Ajayi, J. O., Erigha, E. D., & Obuse, E. (2019). Cloud security baseline development using OWASP, CIS benchmarks, and ISO 27001 for regulatory compliance. IRE Journals, 2(8), 250–256. https://irejournals.com/formatedpaper/1710217.pdf

[26] Essien, I. A., Cadet, E., Ajayi, J. O., Erigha, E. D., & Obuse, E. (2019). Integrated governance, risk, and compliance framework for multi-cloud security and global regulatory alignment. IRE Journals, 3(3), 215–221. https://irejournals.com/formatedpaper/1710218.pdf

[27] Etim, E. D., Essien, I. A., Ajayi, J. O., Erigha, E. D., & Obuse, E. (n.d.). Automation-enhanced ESG compliance models for vendor risk assessment in high-impact infrastructure procurement projects. International Journal of Scientific Research in Computer Science, Engineering and Information Technology. https://doi.org/10.32628/IJSRCSEIT

[28] Etim, E. D., Essien, I. A., Ajayi, J. O., Erigha, E. D., & Obuse, E. (2019). AI-augmented intrusion detection: Advancements in real-time cyber threat recognition. IRE Journals, 3(3), 225–231. https://irejournals.com/formatedpaper/1710369.pdf

[29] Garrett, G.A., 2018. Cybersecurity in the Digital Age: Tools, Techniques, & Best Practices. Aspen Publishers.

[30] Gudepu, B.K., 2016. AI-Powered Anomaly Detection Systems for Insider Threat Prevention. The Computertech, pp.1-9.

[31] Jena, J., 2017. Securing the Cloud Transformations: Key Cybersecurity Considerations for on-Prem to Cloud Migration. International Journal of Innovative Research in Science, Engineering and Technology, 6(10), pp.20563-20568.

[32] Kikitamara, S., van Eekelen, M.C.J.D. and Doomernik, D.I.J.P., 2017. Digital identity management on blockchain for open model energy system. Unpublished Masters thesis–Information Science.

[33] Kulkarni, T., 2016. AI-Powered Cybersecurity Systems: Enhancing Anomaly Detection Through IntelligentAlgorithms. International Journal of Artificial Intelligence and Machine Learning, 6(3).

[34] Lam, J., 2017. Implementing enterprise risk management: From methods to applications. John Wiley & Sons.

[35] Le, N.T. and Hoang, D.B., 2017. Capability maturity model and metrics framework for cyber cloud security. Scalable Computing.

[36] Lins, S., Schneider, S. and Sunyaev, A., 2016. Trust is good, control is better: Creating secure clouds by continuous auditing. IEEE Transactions on Cloud Computing, 6(3), pp.890-903.

[37] McGeveran, W., 2018. The duty of data security. Minn. L. Rev., 103, p.1135.

[38] Mohammed, I.A., 2017. Systematic review of identity access management in information security. International Journal of Innovations in Engineering Research and Technology, 4(7), pp.1-7.

[39] Molina, E. and Jacob, E., 2018. Software-defined networking in cyber-physical systems: A survey. Computers & electrical engineering, 66, pp.407-419.

[40] Morris, K., 2016. Infrastructure as code: managing servers in the cloud. " O'Reilly Media, Inc.".

[41] Nicoletti, B., 2018. Procurement Finance: The Digital Revolution in Commercial Banking. Springer.

[42] Nissen, V., 2017. Digital transformation of the consulting industry—introduction and overview. In Digital Transformation of the Consulting Industry: Extending the Traditional Delivery Model (pp. 1-58). Cham: Springer International Publishing.

[43] Nwokediegwu, Z. S., Bankole, A. O., & Okiye, S. E. (2019). Advancing interior and exterior construction design through large-scale 3D printing: A comprehensive review. IRE Journals, 3(1), 422-449. ISSN: 2456-8880

[44] Omopariola, M. and Lead, C.D., 2016. Zero-Trust Architecture Deployment in Emerging Economies: A Case Study from Nigeria [online]

[45] Onalaja, T. A., Nwachukwu, P. S., Bankole, F. A., & Lateefat, T. (2019). A dual-pressure model for healthcare finance: Comparing United States and African strategies under inflationary stress. IRE Journals, 3(6), 261–270.

[46] Panghal, A., Chhikara, N., Sindhu, N. and Jaglan, S., 2018. Role of Food Safety Management Systems in safe food production: A review. Journal of food safety, 38(4), p.e12464.

[47] Pattaranantakul, M., He, R., Song, Q., Zhang, Z. and Meddahi, A., 2018. NFV security survey: From use case driven threat analysis to state-of-the-art countermeasures. IEEE Communications Surveys & Tutorials, 20(4), pp.3330-3368.

[48] Riemer, K. and Schellhammer, S., 2018. Collaboration in the digital age: diverse, relevant and challenging. In Collaboration in the Digital Age: How Technology Enables Individuals, Teams and Businesses (pp. 1-12). Cham: Springer International Publishing.

[49] Sarfraz, Q., 2017. Design of a Federated Framework for Emergency Response.

[50] Siddiqui, I. and Iqbal, J., 2017. From Data to Strategy: Talent Analytics for Global Enterprise Success.

[51] Singh, B., 2017. Enhancing Real-Time Database Security Monitoring Capabilities Using Artificial Intelligence. INTERNATIONAL JOURNAL OF CURRENT ENGINEERING AND SCIENTIFIC RESEARCH (IJCESR).

[52] Suzic, B., 2016. Towards Secure Integration and Interoperability in Heterogeneous Environments.

[53] Temoshok, D., Temoshok, D. and Abruzzi, C., 2018. Developing trust frameworks to support identity federations. US Department of Commerce, National Institute of Standards and Technology.

[54] Thuraisingham, B., Parveen, P., Masud, M.M. and Khan, L., 2017. Big data analytics with applications in insider threat detection. Auerbach Publications.

[55] Tobin, A. and Reed, D., 2016. The inevitable rise of self-sovereign identity. The Sovrin Foundation, 29(2016), p.18.

[56] Triaa, W., Gzara, L. and Verjus, H., 2016, August. Organizational agility key factors for dynamic business process management. In 2016 IEEE 18th Conference on Business Informatics (CBI) (Vol. 1, pp. 64-73). IEEE.

[57] Tripoli, M. and Schmidhuber, J., 2018. Emerging Opportunities for the Application of Blockchain in the Agri-food Industry.

[58] Tuecke, S., Ananthakrishnan, R., Chard, K., Lidman, M., McCollam, B., Rosen, S. and Foster, I., 2016, October. Globus Auth: A research identity and access management platform. In 2016 IEEE 12th International Conference on e-Science (e-Science) (pp. 203-212). IEEE.

[59] Vasarhelyi, M.A., Alles, M.G. and Kogan, A., 2018. Principles of analytic monitoring for continuous assurance. In Continuous Auditing: Theory and Application (pp. 191-217). Emerald Publishing Limited.

[60] Veale, M., Binns, R. and Ausloos, J., 2018. When data protection by design and data subject rights clash. International Data Privacy Law, 8(2), pp.105-123.

[61] Wachter, S., 2018. Normative challenges of identification in the Internet of Things: Privacy, profiling, discrimination, and the GDPR. Computer law & security review, 34(3), pp.436-449.

@article{1711336,
      author = {Theophilus Onyekachukwu Oshoba, Nafiu Ikeoluwa Hammed, Olushola Damilare Odejobi},
      title = {Secure Identity and Access Management Model for Distributed and Federated Systems},
      journal = {Iconic Research And Engineering Journals},
      year = {2019},
      volume = {3},
      number = {4},
      pages = {550-567},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1711336.pdf},
      abstract = {The proliferation of distributed and federated systems, including cloud computing environments, multi-organization collaborations, and cross-border digital services, has introduced significant challenges in managing identities and controlling access to sensitive resources. Traditional identity and access management (IAM) approaches, which rely on centralized control, are increasingly inadequate in environments characterized by multiple administrative domains, heterogeneous platforms, and dynamic user populations. This study proposes a secure IAM model specifically designed for distributed and federated systems, integrating advanced authentication, authorization, and governance mechanisms to ensure secure, scalable, and compliant access management. The proposed model emphasizes federated identity management, enabling single sign-on (SSO) and secure token exchange across disparate systems while maintaining strict adherence to organizational policies and regulatory standards. Multi-factor authentication (MFA), adaptive risk-based access control, and zero-trust principles are incorporated to enhance security in environments where users, devices, and applications may operate beyond organizational boundaries. Role-based and attribute-based access control frameworks are combined with dynamic policy enforcement to ensure that access rights are context-aware, time-bound, and aligned with compliance requirements such as GDPR, HIPAA, and ISO/IEC 27001. Key technical components include secure identity provisioning, federated trust management, continuous access monitoring, and automated anomaly detection using artificial intelligence and machine learning. The model also provides mechanisms for auditing, reporting, and accountability, enabling organizations to demonstrate regulatory compliance and maintain trust in multi-stakeholder environments. By integrating security, compliance, and operational efficiency, the proposed IAM model supports seamless collaboration, reduces the risk of unauthorized access, and enhances resilience against identity-related threats. The framework offers a scalable and adaptive solution for enterprises and consortiums operating in complex, distributed, and federated systems, establishing a foundation for secure digital transformation and robust governance of identity and access in multi-domain computing ecosystems.},
      keywords = {Secure Identity, Access Management, Distributed Systems, Federated Systems, Authentication, Authorization, Identity Federation, Single Sign-On (SSO), Multi-Factor Authentication (MFA), Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC), Trust Frameworks, Public Key Infrastructure (PKI), Credential Management},
      month = {October},
  }