Bridging the Smart City Cybersecurity Data Gap Through AI-Driven Synthetic Dataset Generation

Computer Science > Cryptography and Security [Submitted on 10 Jun 2026] Bridging the Smart City Cybersecurity Data Gap Through AI-Driven Synthetic Dataset Generation Stephanie Polczynski, John D. Hastings, Varghese Vaidyan, Kyle Korman Smart cities rely on interconnected cyber-physical systems that integrate sensors, IoT devices, cloud platforms, and AI-driven services and decision-making. While these systems enhance city services, they also introduce complex cybersecurity challenges due to their large attack surfaces, heterogeneous data flows, and evolving threat vectors. Developing and validating cybersecurity tools for smart cities requires high-quality datasets that accurately represent real operational conditions. However, real-world datasets are often incomplete, contain privacy-sensitive data, are difficult to access, or lack sufficient malicious activity to support tool development. This research addresses this critical gap by proposing an AI-based synthetic data generation (SDG) framework designed specifically for smart city cybersecurity research. The proposed framework leverages generative artificial intelligence models to produce high-fidelity synthetic cybersecurity datasets that replicate realistic device behaviors, network interactions, and cyber-attack scenarios. The synthetic datasets are evaluated for conformity to protocol standards, statistical similarity to original datasets, and utility in common security tools. The resulting synthetic data generation framework and evaluation metrics are expected to advance smart city cybersecurity by enabling researchers to model threats more effectively and evaluate defensive techniques more comprehensively to better protect critical smart city infrastructures. Comments: 10 pages, 1 figure, 2 tables Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2606.12225 [cs.CR]   (or arXiv:2606.12225v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2606.12225 Focus to learn more Submission history From: John Hastings [view email] [v1] Wed, 10 Jun 2026 15:36:31 UTC (82 KB) Access Paper: view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)