MS Information Systems, 2026
Javed Abdul Tamboli received his Master’s in Information Systems at UMBC. He received his Bachelor’s in Mechanical Engineering from Savitribai Phule Pune University. Prior to his MS degree, he worked for nearly three years in industry, gaining experience in technology and honing skills in Java, Spring Boot, AWS, and data analytics at Cognizant and NVIDIA.
In the KnACC lab, Javed worked on the Smart Manufacturing project where he developed knowledge representation for Manufacturing Compliance.
Publications
- Javed Tamboli, Karuna P. Joshi, and Ommo Clark, “Security Compliance for Smart Manufacturing using Knowledgegraph based Digital Twin“, 2025 IEEE International Conference on Big Data (BigData), December 2025
Javed successfully defended his MS Thesis in April 2025
Title: Security Compliance for Smart Manufacturing using Knowledge Graph based Digital Twin
Committee: Dr. Karuna P Joshi (Chair), Dr. Zhiyuan Chen, Dr. Kai Sun
Abstract:
The convergence of Information Technology (IT) and Operational Technology (OT) in smart manufacturing generates vast, diverse, and rapidly evolving operational data, intensifying cybersecurity and regulatory compliance challenges. Meeting standards such as NIST SP 800-171 is made more difficult by the slow, error-prone nature of manual assessments, which are not designed for the real-time demands of modern assembly-line environments. This thesis presents a Knowledge Graph-based Digital Twin framework that automates cybersecurity compliance verification in smart manufacturing. The framework formalizes three NIST SP 800-171 control families, namely Access Control, Maintenance, and Risk Assessment, as structured policy ontologies using RDF, OWL, and SPARQL on an Apache Jena Fuseki server, integrated with a Smart Manufacturing Knowledge Graph that captures the full IT/OT topology of an assembly line. The framework is validated using a synthetic dataset of 15,000 log entries that reflect the Big Data 5Vs. SPARQL queries are evaluated, covering remote unencrypted intrusion detection, brute-force OT breaches, unsupervised contractor maintenance, and multi-stage attack kill-chain detection across 3,004 events. All queries achieve good precision with no false positives, demonstrating deterministic compliance verification against NIST SP 800-171 requirements. These findings establish a scalable and semantically grounded foundation for automated compliance in integrated smart manufacturing environments.