zkSTAR: A zero knowledge system for time series attack detection enforcing regulatory compliance in critical infrastructure networks
Paritosh Ramanan, H.M. Mohaimanul Islam, Abhiram Reddy Alugula

TL;DR
zkSTAR is a privacy-preserving framework using zk-SNARKs that enables regulators to verify attack detection in critical infrastructure networks without accessing sensitive operational data.
Contribution
It introduces a novel zero-knowledge system for ICS attack detection that enforces detection guarantees while maintaining data confidentiality.
Findings
Successfully verifies detection correctness without data disclosure.
Demonstrates scalability on real-world ICS datasets.
Ensures temporal and statistical consistency through zk-SNARKs.
Abstract
Industrial control systems (ICS) form the operational backbone of critical infrastructure networks (CIN) such as power grids, water supply systems, and gas pipelines. As cyber threats to these systems escalate, regulatory agencies are imposing stricter compliance requirements to ensure system-wide security and reliability. A central challenge, however, is enabling regulators to verify the effectiveness of detection mechanisms without requiring utilities to disclose sensitive operational data. In this paper, we introduce zkSTAR, a cyberattack detection framework that leverages zk-SNARKs to reconcile these requirements and enable provable detection guarantees while preserving data confidentiality. Our approach builds on established residual-based statistical hypothesis testing methods applied to state-space detection models. Specifically, we design a two-pronged zk-SNARK architecture that…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
