Automated Infrastructure Compliance Assessment Using Artificial Intelligence in Industrial IoT Environments
Abstract
Auditing is a core function of governance and compliance in computing and information systems. With the growing reliance of society on systems and services based on the Industrial Internet of Things and Cyber-Physical Systems, new paradigms for auditing such connected systems are as important as they are difficult to achieve. Connected-industrial systems are often very large, their behaviour is constantly changing, completely new services with different characteristics are regularly being introduced, and at times of crisis these services may be configured to operate in an abnormal manner. Compliance with legal and safety-related rules and regulations thus needs to be verified with high frequency and, where possible, with real-time delay. Enormous financial penalties are typically at stake, yet resources are limited. Audit automation allows for a substantial reduction in the frequency and intensity of audits, supports the auditors with detection and verification of abnormal conditions, and with the scope and selection of nodes to be audited. Proposed architectures for audit automation in these domains complement existing AI techniques for auditing of conventional IT.
Compliance verification for the IoT and CPS domains can be facilitated by control-plane support for monitoring and management of critical properties. Such support can both automatically detect deviations from appropriate properties and help auditors execute timely, deep-dive audits of IoT and CPS segments. Proposed architectures for intelligent audit automation assist in detecting and verifying operational compliance of industrial-systems services. The applicability of these solutions goes much beyond operational compliance, however, and includes safety and reliability, cybersecurity, and environmental and energy compliance for industrial systems.
Article Information
Journal |
International Journal of Emerging Trends in Engineering and Management Research |
|---|---|
Volume (Issue) |
Vol. 9 No. 2 (2024): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) |
DOI |
|
Pages |
15367-15382 |
Published |
April 10, 2024 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Vinod Battapothu (%2024). Automated Infrastructure Compliance Assessment Using Artificial Intelligence in Industrial IoT Environments. International Journal of Emerging Trends in Engineering and Management Research , Vol. 9 No. 2 (2024): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 15367-15382. https://doi.org/10.15662/ijetemr.2024.0902002 |
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3. Alanazi, R., & Aljuhani, A. (2023). Anomaly detection for Industrial Internet of Things cyberattacks. Computer Systems Science and Engineering, 44(3), 2361–2378.
4. Davuluri, P. S. L. N. (2023). Integrating artificial intelligence into event-driven financial crime compliance platforms. International Journal of Finance, 36(6), 707-736.
5. Alzubaidi, A., Mitra, K., & Solaiman, E. (2023). A blockchain-based SLA monitoring and compliance assessment for IoT ecosystems. Journal of Cloud Computing, 12, 50.
6. Inala, R. (2023). AI-powered investment decision support systems: Building smart data products with embedded governance controls. Journal for ReAttach Therapy and Developmental Diversities, 6(10), 2251-2266.
7. Amistapuram, K. (2023). Privacy-Preserving Machine Learning Models for Sensitive Customer Data in Insurance Systems. Educational Administration: Theory and Practice, 29(4), 5950-5958.
8. Nagabhyru, K. C. (2023). From Data Silos to Knowledge Graphs: Architecting CrossEnterprise AI Solutions for Scalability and Trust. Available at SSRN.
9. Chen, Z., Li, Z., Huang, J., Liu, S., & Long, H. (2024). An effective method for anomaly detection in industrial Internet of Things using XGBoost and LSTM. Scientific Reports, 14, 23969.
10. Kaulwar, P. K., Pamisetty, A., Mashetty, S., Adusupalli, B., & Pandiri, L. (2023). Harnessing intelligent systems and secure digital infrastructure for optimizing housing finance, risk mitigation, and enterprise supply networks. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 372-402.
11. Kolla, S. H. (2022). Strategic Information Integration Models for Cross-Functional Service Optimization in Large-Scale Enterprises. International Journal of Emerging Trends in Engineering and Management Research, 7(3), 11811.
12. Garapati, R. S. (2023). Optimizing Energy Consumption in Smart Build-ings Through Web-Integrated AI and Cloud-Driven Control Systems.
13. Daoudagh, S., Marchetti, E., Calabrò, A., Ferrada, F., Oliveira, A. I., Barata, J., Peres, R., & Marques, F. (2023). DAEMON: A domain-based monitoring ontology for IoT systems. SN Computer Science, 4, 632.
14. Aitha, A. R. (2022). Cloud Native ETL Pipelines for Real Time Claims Processing in Large Scale Insurers. Universal Journal of Business and Management.
15. Vijaya Rama Raju Gottimukkala, “Energy-Efficient Design Patterns for Large-Scale Banking Applications Deployed on AWS Cloud,” International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2020.91225
16. Mattaparthi, R. (2023). Connected Fleet Intelligence: Edge-Centric Analytics and Computer Vision for Predictive Manufacturing and Asset Resilience. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(5), 9077-9088.
17. Echenim, K. U., & Joshi, K. P. (2023). IoT-Reg: A comprehensive knowledge graph for real-time IoT data privacy compliance. 2023 IEEE International Conference on Big Data (BigData), 2897–2906.
18. Segireddy, A. R. (2020). Cloud Migration Strategies for High-Volume Financial Messaging Systems.
19. Kolla, S. K., & Reddy, V. A. R. (2023). Deep Learning Architectures For Multimodal Medical Data Integration. South Eastern European Journal of Public Health, 248-260.
20. Yandamuri, U. S. (2023). An Intelligent Analytics Framework Combining Big Data and Machine Learning for Business Forecasting. International Journal Of Finance, 36(6), 682-706.
21. Federici, F., Martintoni, D., & Senni, V. (2023). A zero-trust architecture for remote access in Industrial IoT infrastructures. Electronics, 12(3), 566.
22. Kolla, T., & Kolla, S. K. (2023). FHIR-Based Real-Time Healthcare Analytics using Unsupervised Learning. International Journal of Future Innovative Science and Technology (IJFIST), 6(6), 11751.
23. Bandi, V. D. V. K. (2023, November). Production-Grade Machine Learning Pipelines For Healthcare Predictive Analytics. South Eastern European Journal of Public Health, 189–205.
24. Mangala, N. (2021). Optimizing Large-Scale ETL Pipelines Using Medallion Architecture on Azure Data Lake. Journal of Artificial Intelligence and Big Data, 1(1), 1-20.
25. Loganathan, R. (2022). Converging Security Architecture and Compliance Management in Enterprise Data Center Ecosystems: A Unified Control Framework. International Journal of Scientific Research and Modern Technology, 1(12), 295-312.
26. Gyamfi, E., & Jurcut, A. (2022). Intrusion detection in Internet of Things systems: A review on design approaches leveraging multi-access edge computing, machine learning, and datasets. Sensors, 22(10), 3744.
27. Reddy, V. A. R. (2023). Orchestrating the Future Autonomous Healthcare Data Pipeline Management through Agentic AI Architectures. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(2), 7979-7992.
28. Alenezi, N., & Aljuhani, A. (2023). Intelligent intrusion detection for Industrial Internet of Things using clustering techniques. Computer Systems Science and Engineering, 46(3), 2899–2915.
29. Jeffrey, N., Tan, Q., & Villar, J. R. (2023). A review of anomaly detection strategies to detect threats to cyber-physical systems. Electronics, 12(15), 3283.
30. Kolla, T. (2023). Predictive ETL Failure Detection in Healthcare Data Pipelines Using Anomaly Detection Algorithms. International Journal of Medical Toxicology & Legal Medicine.
31. Inala, R. (2023). Revolutionizing Customer Master Data in Insurance Technology Platforms: An AI and MDM Architecture Perspective. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 579-606.
32. Garapati, R. S. (2022). Web-Centric Cloud Framework for Real-Time Monitoring and Risk Prediction in Clinical Trials Using Machine Learning. Current Research in Public Health, 2, 1346.
33. Knoblauch, D., & Großmann, J. (2023). Towards a risk-based continuous auditing-based certification for machine learning. The Review of Socionetwork Strategies, 17, 255–273.
34. Aitha, A. R. (2021). Optimizing Data Warehousing for Large Scale Policy Management Using Advanced ETL Frameworks.
35. Nagabhyru, K. C., & Engineer, S. D. (2023). Unifying Data Engineering and Machine Learning Pipelines: An Enterprise Roadmap to Automated Model Deployment.
36. Li, B., Chang, Y., Huang, H., Li, W., Li, T., & Chen, W. (2023). Artificial immunity based distributed and fast anomaly detection for Industrial Internet of Things. Future Generation Computer Systems, 148, 367–379.
37. Gottimukkala, V. R. R. (2021). Digital Signal Processing Challenges in Financial Messaging Systems: Case Studies in High-Volume SWIFT Flows. Universal Journal of Finance and Economics.
38. Kolla, S. H. (2023). Large Language Model-Driven Enterprise Service Intelligence for Digital Workflow Transformation. International Journal of Research and Applied Innovations, 6(1), 8380-8391.
39. Mendonça, R. V., Silva, J. C., Rosa, R. L., Saadi, M., Rodriguez, D. Z., & Farouk, A. (2022). A lightweight intelligent intrusion detection system for industrial internet of things using deep learning algorithms. Expert Systems, 39(5), e12917.
40. Al-Hawawreh, M., Sitnikova, E., & Aboutorab, N. (2022). X-IIoTID: A connectivity-agnostic and device-agnostic intrusion data set for Industrial Internet of Things. IEEE Internet of Things Journal, 9(5), 3962–3977.
41. Amistapuram, K. (2021). Digital Transformation in Insurance: Migrating Enterprise Policy Systems to. NET Core. Universal Journal of Computer Sciences and Communications, 1(1), 1-17.
42. Minkkinen, M., Laine, J., & Mäntymäki, M. (2022). Continuous auditing of artificial intelligence: A conceptualization and assessment of tools and frameworks. Digital Society, 1, 21.
43. Yandamuri, U. S. (2022). Cloud-Based Data Integration Architectures for Scalable Enterprise Analytics. International Journal of Intelligent Systems and Applications in Engineering, 10, 472-483.
44. DAVULURI, P. N. (2022). Cloud-Native Data Platform Modernization for Regulatory Compliance in Global Banking. Kurdish Studies.
45. Ni, C., & Li, S. C. (2024). Machine learning enabled Industrial IoT security: Challenges, trends and solutions. Journal of Industrial Information Integration, 38, 100549.
46. Divya, V., & Bandi, V. K. (2023). Cloud-Native Model Lifecycle Management for Enterprise AI Systems. International Journal of Scientific Research and Modern Technology, 78.
47. Mattaparthi, R. (2023). Deep Learning-Driven Combustion Anomaly Detection in Diesel Powertrains: A Multi-Sensor Fusion Approach for Real-Time ECM Adaptation. International Journal of Intelligent Systems and Applications in Engineering, 11, 1084.
48. Rizvi, S., Zwerling, T., Thompson, B., Faiola, S., Campbell, S., Fisanick, S., & Hutnick, C. (2023). A modular framework for auditing IoT devices and networks. Computers & Security, 132, 103327.
49. Kolla, S. K. (2023). Learning Health Systems Machine Intelligence for Clinical Prediction and Healthcare Optimization. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(2), 7955-7966.
50. Mangala, N. (2022). Real-Time Data Quality Monitoring and Gating Frameworks in Cloud-Based Data Pipelines. International Journal of Research and Applied Innovations, 5(6), 8197-8219.
51. Rodríguez, M., Tobón, D. P., & Múnera, D. (2023). Anomaly classification in industrial Internet of Things: A review. Intelligent Systems with Applications, 18, 200232.
52. Peddi, R. K. (2021). Optimizing Case Management Workflows in Global Data Center Colocation Services. Universal Journal of Computer Sciences and Communications, 1(1), 1-21.
53. Reddy, V. A. R. (2023). Predictive Healthcare Administration Using Advanced Payer Analytics and Population Health Data Engineering. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(2), 7967-7978.
54. Sikos, L. F. (2023). Cybersecurity knowledge graphs. Knowledge and Information Systems, 65, 3511–3531.
55. Mangalampalli, B. M. (2022). Automated Invoice Validation Systems Using Advanced SQL Analytics in Healthcare Insurance. Front Health Inform, 11.
56. Mashetty, S. (2023). Leveraging Data Analytics to Enhance Affordable Housing Initiatives and Community Development. Available at SSRN 5249221.
57. Sun, Z., Ran, G., & Jin, Z. (2022). Intrusion detection method based on active incremental learning in Industrial Internet of Things environment. Journal on Internet of Things, 4(2), 99–111.
58. Loganathan, R. (2021). Integrated Risk and Compliance Frameworks for Global Data Center Operations: A Governance-Centric Approach. Universal Journal of Computer Sciences and Communications, 1(1), 1-26.
59. Kolla, S. H., & Loganathan, R. (2023). Cloud-Native Deep Learning Architectures For Secure Generative AI Deployment In Enterprise Workflow Platforms. Journal of International Crisis and Risk Communication Research, 603-618.
60. Vitale, F., De Vita, F., Mazzocca, N., & Bruneo, D. (2023). A process mining-based unsupervised anomaly detection technique for the Industrial Internet of Things. Internet of Things, 24, 100993.
61. Garapati, R. S. (2022). AI-Augmented Virtual Health Assistant: A Web-Based Solution for Personalized Medication Management and Patient Engagement. Available at SSRN.
62. Valiki, D., & Segireddy, A. R. (2023). Deep Learning Architectures Deployed on Cloud Platforms for Dynamic Financial Risk Evaluation and Market Prediction. American International Journal of Computer Science and Technology, 5(5), 12-24.
63. Nagabhyru, K. C. (2022). Bridging Traditional ETL Pipelines with AI Enhanced Data Workflows: Foundations of Intelligent Automation in Data Engineering. Available at SSRN 5505199.
64. Reddy, V. A. R. (2021). Challenges in Standardizing Member Eligibility Data Across Multi-Payer Healthcare Ecosystems. International Journal of Medical Toxicology and Legal Medicine, 24(3), 1-19.
65. Yandamuri, U. S. (2021). A Comparative Study of Traditional Reporting Systems versus Real-Time Analytics Dashboards in Enterprise Operations. Universal Journal of Business and Management, 1(1), 1-13.
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