AI-Driven Adaptive Security Frameworks for Decentralized Edge Computing in IoT Ecosystems
DOI:
https://doi.org/10.32628/CSEIT2612114Keywords:
AI-driven security, Edge computing, Internet of Things, Adaptive defense mechanisms, Trust management, Privacy-preserving model sharingAbstract
The rapid proliferation of Internet of Things (IoT) devices and the growing adoption of decentralized edge computing architectures have significantly transformed data processing, latency optimization, and real-time decision-making across smart ecosystems. However, this paradigm shift has also introduced complex security challenges, including heterogeneous device vulnerabilities, dynamic network topologies, limited computational resources, and increased exposure to cyberattacks at the edge. This review paper examines AI-driven adaptive security frameworks as a promising approach for securing decentralized edge computing environments within IoT ecosystems. It systematically analyzes how artificial intelligence techniques—such as machine learning, deep learning, federated learning, and reinforcement learning—enable real-time threat detection, anomaly identification, intrusion prevention, and automated security policy adaptation at the network edge. The review highlights architectural models that integrate AI with edge and fog computing to achieve scalable, low-latency, and privacy-preserving security mechanisms while reducing reliance on centralized cloud infrastructures. Furthermore, it evaluates existing frameworks, recent case studies, and emerging trends, emphasizing challenges related to data privacy, model robustness, explainability, energy efficiency, and interoperability. By synthesizing current research and identifying open research gaps, this paper provides a comprehensive foundation for developing resilient, intelligent, and adaptive security solutions that can support the evolving requirements of decentralized IoT edge computing ecosystems.
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Abiodun, K., Alaka, E., Jinadu, S. O., Igba, E., & Ezeh, V. N. (2025). A review of federated learning approaches for predictive modeling and confidential data analysis in lending and borrowing behavior across decentralized financial networks. Finance & Accounting Research Journal, 7(3). https://doi.org/10.51594/farj.v7i3 DOI: https://doi.org/10.51594/farj.v7i6.1968
Ajayi, A. A., Igba, E., Soyele, A. D., & Enyejo, J. O. (2024). Quantum cryptography and blockchain-based social media platforms as a dual approach to securing financial transactions in CBDCs and combating misinformation in U.S. elections. International Journal of Innovative Science and Research Technology, 9(10). https://doi.org/10.38124/ijisrt/IJISRT24OCT1697 DOI: https://doi.org/10.38124/ijisrt/IJISRT24OCT1697
Akindotei, O., Igba, E., Awotiwon, B. O., & Otakwu, A. (2024). Blockchain integration in critical systems enhancing transparency, efficiency, and real-time data security in agile project management, decentralized finance (DeFi), and cold chain management. International Journal of Scientific Research and Modern Technology (IJSRMT), 3(11). https://doi.org/10.38124/ijsrmt.v3i11.107 DOI: https://doi.org/10.38124/ijsrmt.v3i11.107
Alade, O., & Ijiga, O. M. (2025). A physics-embedded AI framework for predictive polymer thermodynamics: Methodology, system design, and validation. International Journal on Science and Technology, 16(4). DOI: https://doi.org/10.71097/IJSAT.v16.i4.9928
Alaka, E., Abiodun, K., Jinadu, S. O., Igba, E., & Ezeh, V. N. (2025). Data integrity in decentralized financial systems: A model for auditable, automated reconciliation using blockchain and AI. International Journal of Management and Commerce Innovations, 13(1), 136–158. https://doi.org/10.5281/zenodo.15753099
Alrawais, A., Alhothaily, A., Hu, C., & Cheng, X. (2020). Fog computing for the Internet of Things: Security and privacy issues. IEEE Internet of Things Journal, 7(10), 10435–10447. https://doi.org/10.1109/JIOT.2020.3004141
Aluso, L., & Enyejo, J. O. (2023). Integrating ETL workflows with LLM-augmented data mapping for automated business intelligence systems. International Journal of Scientific Research and Modern Technology, 2(11), 76–89. https://doi.org/10.38124/ijsrmt.v2i11.1078 DOI: https://doi.org/10.38124/ijsrmt.v2i11.1078
Amebleh, J. (2024). Latency-aware, tokenization-first payment orchestration for telehealth: Idempotency guarantees, PCI/HIPAA telemetry, and end-to-end latency SLOs. Global Journal of Engineering, Science & Social Science Studies, 9(9). http://doi.org/10.63665/gjess.v09i9.1 DOI: https://doi.org/10.63665/gjess.v09i9.1
Amebleh, J., & Igba, E. (2024). Causal uplift for rewards aggregators: Doubly-robust heterogeneous treatment-effect modeling with SQL/Python pipelines and real-time inference. International Journal of Scientific Research and Modern Technology, 3(5), 39–55. https://doi.org/10.38124/ijsrmt.v3i5.819 DOI: https://doi.org/10.38124/ijsrmt.v3i5.819
Anokwuru, E. A., & Enyejo, J. O. (2025). Predictive modeling for portfolio risk assessment in multi-therapeutic pharmaceutical enterprises. International Journal of Innovative Science and Research Technology, 10(11), 2354–2370. https://doi.org/10.38124/ijisrt/25nov1475 DOI: https://doi.org/10.38124/ijisrt/25nov1475
Anokwuru, E. A., & Igba, E. (2025). AI-driven field enablement systems for oncology commercial strategy: A framework for enhancing decision-making and market execution. International Journal of Scientific Research and Modern Technology, 4(2), 118–135. https://doi.org/10.38124/ijsrmt.v4i2.1011 DOI: https://doi.org/10.38124/ijsrmt.v4i2.1011
Anokwuru, E. A., & Okoh, O. F. (2023). Sustainable product development models in healthcare technology: Quantifying the environmental and operational impact of green design integration. International Journal of Scientific Research in Science and Technology, 10(6), 919–939. https://ijsrst.com/IJSRST2291346
Anokwuru, E. A., Owusua Mends, K. Y., & Okoh, O. F. (2023). AI-integrated market access strategies in oncology: Using predictive analytics to navigate pricing, reimbursement and competitive landscapes. International Journal of Scientific Research and Modern Technology, 2(12), 49–63. https://doi.org/10.38124/ijsrmt.v2i12.1037 DOI: https://doi.org/10.38124/ijsrmt.v2i12.1037
Biggio, B., & Roli, F. (2018). Wild patterns: Ten years after the rise of adversarial machine learning. Pattern Recognition, 84, 317–331. https://doi.org/10.1016/j.patcog.2018.07.023 DOI: https://doi.org/10.1016/j.patcog.2018.07.023
Dave, D. M. K., & Mittapally, B. K. (2024). Data integration and interoperability in IoT: challenges, strategies and future direction. Int. J. Comput. Eng. Technol.(IJCET), 15, 45-60.
Donkor, F., Okafor, M. N., & Enyejo, J. O. (2025). Exploring metabolomics guided authentication of plant-based meat alternatives supporting regulatory standards and consumer health protection. International Journal of Innovative Science and Research Technology, 10(10). https://doi.org/10.38124/ijisrt/25oct1027 DOI: https://doi.org/10.38124/ijisrt/25oct1027
Donkor, F., Okafor, M. N., & Enyejo, J. O. (2025). Investigating nanotechnology-based smart packaging for extending dairy product shelf life and improving food quality assurance. International Journal of Healthcare Sciences: Research Publish Journals, 13(2), 17–34. https://doi.org/10.5281/zenodo.17381311
Fernandes, E., Soares, L., Gomes, J., Freire, M., & Inácio, P. (2021). Security and privacy in edge computing: A systematic review. Journal of Network and Computer Applications, 177, 102912. https://doi.org/10.1016/j.jnca.2020.102912 DOI: https://doi.org/10.1016/j.jnca.2020.102912
Ferrag, M. A., Maglaras, L., Moschoyiannis, S., & Janicke, H. (2021). Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study. Journal of Information Security and Applications, 50, 102419. https://doi.org/10.1016/j.jisa.2019.102419 DOI: https://doi.org/10.1016/j.jisa.2019.102419
Gabla, E. S., Peter-Anyebe, A. C., & Ijiga, O. M. (2025). Assessing machine learning enabled anomaly detection models for real-time cyberattack mitigation in optical fiber communication systems. World Journal of Advanced Engineering Technology and Sciences, 17(02), 001–017. https://doi.org/10.30574/wjaets.2025.17.2.1454 DOI: https://doi.org/10.30574/wjaets.2025.17.2.1454
García-Teodoro, P., Díaz-Verdejo, J., Maciá-Fernández, G., & Vázquez, E. (2020). Anomaly-based network intrusion detection: Techniques, systems and challenges. Computers & Security, 28(1–2), 18–28. https://doi.org/10.1016/j.cose.2008.08.003 DOI: https://doi.org/10.1016/j.cose.2008.08.003
Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2018). A survey of methods for explaining black box models. ACM Computing Surveys, 51(5), 1–42. https://doi.org/10.1145/3236009 DOI: https://doi.org/10.1145/3236009
Idika, C. N., Salami, E. O., Ijiga, O. M., & Enyejo, L. A. (2021). Deep learning driven malware classification for cloud-native microservices in edge computing architectures. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 7(4). https://doi.org/10.32628/CSEIT182551 DOI: https://doi.org/10.32628/CSEIT182551
Igba, E., Abiodun, K., & Ali, E. O. (2025). Building the backbone of the digital economy and financial innovation through strategic investments in data centers. International Journal of Innovative Science and Research Technology. https://doi.org/10.5281/zenodo.14651210
Igba, E., Ihimoyan, M. K., Awotinwo, B., & Apampa, A. K. (2024). Integrating BERT, GPT, Prophet algorithm, and finance investment strategies for enhanced predictive modeling and trend analysis in blockchain technology. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(6), 1620–1645. https://doi.org/10.32628/CSEIT241061214 DOI: https://doi.org/10.32628/CSEIT241061214
Ilesanmi, M. O., Anim-Sampong, S. D., & Enyejo, J. O. (2023). Cross-sector asset management: Applying real estate portfolio optimization models to renewable energy infrastructure. International Journal of Scientific Research and Modern Technology, 2(10), 1–17. https://doi.org/10.38124/ijsrmt.v2i10.1077 DOI: https://doi.org/10.38124/ijsrmt.v2i10.1077
Ilesanmi, M. O., Okoh, O. F., & Ijiga, O. M. (2025). Financial risk modeling for hybrid renewable energy portfolios under evolving U.S. regulatory and tax equity structures. International Journal of Innovative Science and Research Technology, 10(10). https://doi.org/10.38124/ijisrt/25oct1443 DOI: https://doi.org/10.38124/ijisrt/25oct1443
Ilesanmi, M. O., Okoh, O. F., Balogun, S. A., & Ijiga, O. M. (2024). Data-driven portfolio optimization for utility-scale solar, wind, and battery energy storage systems (BESS): Integrating performance analytics with investor EBITDA targets. International Journal of Scientific Research and Modern Technology, 3(11), 141–155. https://doi.org/10.38124/ijsrmt.v3i11.943
Ilesanmi, M. O., Okoh, O. F., Balogun, S. A., & Ijiga, O. M. (2024). Data-driven portfolio optimization for utility-scale solar, wind, and battery energy storage systems (BESS): Integrating performance analytics with investor EBITDA targets. International Journal of Scientific Research and Modern Technology, 3(11), 141–155. https://doi.org/10.38124/ijsrmt.v3i11.943 DOI: https://doi.org/10.38124/ijsrmt.v3i11.943
James, U. U. (2022). Federated identity management using secure enclaves for cross-domain access control in 5G edge networks. International Journal of Scientific Research in Science, Engineering and Technology, 9(6). https://doi.org/10.32628/IJSRSET25122271 DOI: https://doi.org/10.32628/IJSRSET25122271
Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., … Zhao, S. (2021). Advances and open problems in federated learning. Foundations and Trends® in Machine Learning, 14(1–2), 1–210. https://doi.org/10.1561/2200000083 DOI: https://doi.org/10.1561/2200000083
Mends Karen, Y. O., Anokwuru, E. A., & Igba, E. (2025). Integrating real world data and real world evidence into commercial product strategy: A predictive framework for pharmaceutical market forecasting. International Journal of Healthcare Sciences, 13(2), 334–353. https://doi.org/10.5281/zenodo.17876964
Nwatuzie, G. A., Ijiga, O. M., Idoko, I. P., Enyejo, L. A., & Ali, E. O. (2025). Design and evaluation of a user-centric cryptographic model leveraging hybrid algorithms for secure cloud storage and data integrity. American Journal of Innovation in Science and Engineering (AJISE), 4(1). https://doi.org/10.54536/ajise.v4i2.4482 DOI: https://doi.org/10.54536/ajise.v4i2.4482
Ocharo, D. O., & Omachi, A. (2022). Optimization of microgrid-controlled chiller plants for data center cooling in the Northeastern United States. International Journal of Scientific Research in Science and Technology, 9(3), 865–880. https://doi.org/10.32628/IJSRST229345 DOI: https://doi.org/10.32628/IJSRST229345
Ocharo, D. O., Onyia, V. O., Bamigwojo, V. O., Adaudu, I. I., & Avevor, J. (2023). Structural and thermal behavior of building-integrated photovoltaic (BIPV) facades in high-rise urban buildings in Philadelphia. International Journal of Scientific Research in Civil Engineering, 7(5), 161–192. https://doi.org/10.32628/IJSRCE237418 DOI: https://doi.org/10.32628/IJSRCE237418
Ogbuonyalu, U. O., Abiodun, K., Dzamefe, S., Vera, E. N., Oyinlola, A., & Igba, E. (2024). Assessing artificial intelligence driven algorithmic trading implications on market liquidity risk and financial systemic vulnerabilities. International Journal of Scientific Research and Modern Technology, 3(4), 18–21. https://doi.org/10.38124/ijsrmt.v3i4.433 DOI: https://doi.org/10.38124/ijsrmt.v3i4.433
Ogbuonyalu, U. O., Abiodun, K., Dzamefe, S., Vera, E. N., Oyinlola, A., & Igba, E. (2025). Integrating decentralized finance protocols with systemic risk frameworks for enhanced capital markets stability and regulatory oversight. International Journal of Innovative Science and Research Technology, 10(4). https://doi.org/10.38124/ijisrt/25apr1165 DOI: https://doi.org/10.38124/ijisrt/25apr1165
Okoh, O. F., Ukpoju, E. A., Otakwu, A., Ayoola, V. B., & Ijiga, A. C. (2024). Evaluating the influence of human capital development on economic growth: A global analysis of the potential impact of artificial intelligence technologies. Corporate Sustainable Management Journal (CSMJ), 2(1), 49–59. http://doi.org/10.26480/csmj.01.2024.49.59 DOI: https://doi.org/10.26480/csmj.01.2024.49.59
Okpanachi, A. T., Adeniyi, M., Igba, E., & Dzakpasu, N. H. (2025). Enhancing blood supply chain management with blockchain technology to improve diagnostic precision and strengthen health information security. International Journal of Innovative Science and Research Technology, 10(4). https://doi.org/10.38124/ijisrt/25apr214 DOI: https://doi.org/10.38124/ijisrt/25apr214
Okpanachi, A. T., Igba, E., Imoh, P. O., Dzakpasu, N. H., & Nyaledzigbor, M. (2025). Leveraging digital biomarkers and advanced data analytics in medical laboratory to enhance early detection and diagnostic accuracy in cardiovascular diseases. International Journal of Scientific Research in Science and Technology, 12. https://doi.org/10.32628/IJSRST251222590 DOI: https://doi.org/10.32628/IJSRST251222590
Oyekan, M., Igba, E., & Jinadu, S. O. (2024). Building resilient renewable infrastructure in an era of climate and market volatility. International Journal of Scientific Research in Humanities and Social Sciences, 1(1). https://doi.org/10.32628/IJSRSSH243563 DOI: https://doi.org/10.32628/IJSRSSH243563
Roman, R., Lopez, J., & Mambo, M. (2020). Mobile edge computing, fog et al.: A survey and analysis of security threats and challenges. ACM Computing Surveys, 52(5), 1–37. https://doi.org/10.1145/3305264
Roman, R., Lopez, J., & Mambo, M. (2020). Mobile edge computing, fog et al.: A survey and analysis of security threats and challenges. ACM Computing Surveys, 52(5), 1–37. https://doi.org/10.1145/3305264
Satyanarayanan, M. (2021). The emergence of edge computing. Computer, 54(1), 30–39. https://doi.org/10.1109/MC.2021.3057163 DOI: https://doi.org/10.1109/MC.2017.9
Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2020). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637–646. https://doi.org/10.1109/JIOT.2016.2579198 DOI: https://doi.org/10.1109/JIOT.2016.2579198
Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2021). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 8(5), 3647–3668. https://doi.org/10.1109/JIOT.2020.3027221
Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2021). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 8(5), 3647–3668. https://doi.org/10.1109/JIOT.2020.3027221
Singh, A., Satapathy, S. C., Roy, A., & Gutub, A. (2022). Ai-based mobile edge computing for iot: Applications, challenges, and future scope. Arabian Journal for Science and Engineering, 47(8), 9801-9831. DOI: https://doi.org/10.1007/s13369-021-06348-2
Ussher-Eke, D., Igba, E. O., Ijiga, O. M., & Enyejo, J. O. (2025). Improving employee engagement and safety through the use of IoT-enabled monitoring tools in human resource practices. Journal of Technology & Innovation, 5(2), 48–55. https://doi.org/10.26480/jtin.02.2025.48.55
Ussher-Eke, D., James, U. U., & Okoh, O. F. (2024). Zero trust onboarding in HR tech: Safeguarding applicant tracking systems against deepfake resumes and credential fraud. International Journal of Scientific Research in Humanities and Social Sciences, 1(1), 262–281. https://doi.org/10.32628/IJSRSSH243565
Ussher-Eke, D., James, U. U., & Okoh, O. F. (2024). Zero trust onboarding in HR tech: Safeguarding applicant tracking systems against deepfake resumes and credential fraud. International Journal of Scientific Research in Humanities and Social Sciences, 1(1), 262–281. https://doi.org/10.32628/IJSRSSH24356 DOI: https://doi.org/10.32628/IJSRSSH243565
Uzoma, E., Igba, E., & Olola, T. M. (2024). Analyzing edge AI deployment challenges within hybrid IT systems utilizing containerization and blockchain-based data provenance solutions. International Journal of Scientific Research and Modern Technology, 3(12), 125–141. https://doi.org/10.38124/ijsrmt.v3i12.408 DOI: https://doi.org/10.38124/ijsrmt.v3i12.408
Uzoma, E., Ijiga, O. M., Terver, S., & Peverga, J. (2025). Blockchain-enabled nanocatalyst monitoring system for real-time dye degradation in industrial wastewater. American Journal of Innovation in Science and Engineering, 4(3), 78–94. https://doi.org/10.54536/ajise.v4i3.5836 DOI: https://doi.org/10.54536/ajise.v4i3.5836
Woz U (2022) https://woz-u.com/blog/machine-learning-important/
Xu, R., Nagothu, D., & Chen, Y. (2024). AR-Edge: Autonomous and Resilient Edge Computing. Edge Computing Architecture-Architecture and Applications for Smart Cities: Architecture and Applications for Smart Cities, 15. DOI: https://doi.org/10.5772/intechopen.1005876
Yahuza, M., Idris, M. Y. I. B., Wahab, A. W. B. A., Ho, A. T., Khan, S., Musa, S. N. B., & Taha, A. Z. B. (2020). Systematic review on security and privacy requirements in edge computing: State of the art and future research opportunities. IEEE Access, 8, 76541-76567. DOI: https://doi.org/10.1109/ACCESS.2020.2989456
Zarpelão, B. B., Miani, R. S., Kawakani, C. T., & De Alvarenga, S. C. (2017). A survey of intrusion detection in Internet of Things. Journal of Network and Computer Applications, 84, 25-37. DOI: https://doi.org/10.1016/j.jnca.2017.02.009
Zhang, J., Chen, B., Zhao, Y., Cheng, X., & Hu, F. (2018). Data security and privacy-preserving in edge computing paradigm: Survey and open issues. IEEE access, 6, 18209-18237. DOI: https://doi.org/10.1109/ACCESS.2018.2820162
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