arXiv subjects

Sathwik Yamana

Publications and source records attributed to Sathwik Yamana.

3 recordsLinked to original sources

arXiv2026

zkSTAR: A zero knowledge system for time series attack detection enforcing regulatory compliance in critical infrastructure networks

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 zero-knowledge based cyberattack detection framework that leverages zk-SNARKs to enable regulatory compliance while delivering provable detection guarantees with complete data privacy. 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 enforces (i) temporal consistency of the state-space dynamics and (ii) statistical consistency of the detection tests, enabling regulators to verify correctness and prevent suppression of alarms without visibility into utility-level data. We formally analyze the soundness and zero-knowledge properties of our framework and validate its practical feasibility through computational experiments on real-world ICS datasets. Our work demonstrates that zkSNARKs can provide a compliant, scalable, privacy-preserving alternative for detecting data-driven cyberattacks on ICS driven critical infrastructure networks.

cs.CR↗

arXiv2026

Privacy-Aware Collaborative and Distributed Bayesian Optimization

We propose a collaborative meta-learning framework for distributed Bayesian optimization matching centralized performance without raw-data exchange. We show gradient sharing leaks client observations, with leakage worsening as the search converges and queries concentrate near the optimum. We evaluate a differentially private defense and characterize its privacy-utility trade-off.

cs.LG↗

arXiv2026

PRECISE: Private Regulatory Compliance for Cyberattack Detection on Critical Infrastructure Systems

Industrial control systems are a fundamental component of critical infrastructure networks (CIN) such as gas, water, and power. With the growing risk of cyberattacks, regulatory compliance requirements are also increasing for large scale critical infrastructure systems comprising multiple utility stakeholders. The primary goal of regulators is to ensure overall system stability with recourse to trustworthy stakeholder attack detection. However, adhering to compliance requirements requires stakeholders to also disclose sensor and control data to regulators, raising privacy concerns. In this paper, we present a cyberattack detection framework PRECISE, that utilizes differentially private (DP) hypothesis tests geared towards enhancing regulatory confidence while alleviating privacy concerns of CIN stakeholders. The hallmark of our approach is a two phase privacy scheme applying Laplacian DP to covariance matrix disclosures and Gaussian Differential Privacy (GDP) to state-space residuals derived from a Non-Linear Kalman Filter LSTM model. We formally characterize the GDP-induced test statistic via the non-central chi-squared distribution and derive tight bounds on misclassification rates and equivalent DP levels of significance. Theoretically, we show that our method induces a misclassification error rate comparable to the non-DP cases while delivering robust privacy guarantees. Using real-world HAI and ORNL-PS datasets, we demonstrate that under strong differential privacy guarantees on both covariance and residual disclosures, PRECISE matches non-DP detection outcomes in over 88% of cases within 600 seconds of attack onset for the HAI dataset and over 92% for the ORNL-PS dataset, while maintaining false alarm rates below 9% across all tested DP parameter configurations.

cs.CR↗