Pubblicazioni

Autori:

Donadel, Denis; Crestanello, Gabriele; Morandini, Giulio; Antonioli, Daniele; Conti, Mauro; Merro, Massimo

Titolo:

SimProcess: High Fidelity Simulation of Noisy ICS Physical Processes

Anno:

2025

Tipologia prodotto:

Contributo in atti di convegno

Tipologia ANVUR:

Contributo in Atti di convegno

Lingua:

Inglese

Formato:

A Stampa

Titolo del Convegno:

11th ACM Cyber-Physical System Security Workshop

Luogo:

Ha Noi, Vietnam

Periodo:

26 August 2025

Casa editrice:

ACM Press

Intervallo pagine:

1-12

Parole chiave:

Industrial Control System; Honeypot; Simulation; Physical process; Noice; Power grid

Breve descrizione dei contenuti:

Industrial Control Systems (ICS) manage critical infrastructures like power grids and water treatment plants. Cyberattacks on ICSs can disrupt operations, causing severe economic, environmental, and safety issues. For example, undetected pollution in a water plant can put the lives of thousands at stake. ICS researchers have increasingly turned to honeypots—decoy systems designed to attract attackers, study their behaviors, and eventually improve defensive mechanisms. However, existing ICS honeypots struggle to replicate the ICS physical process, making them susceptible to detection. Accurately simulating the noise in ICS physical processes is challenging because different factors produce it, including sensor imperfections and external interferences. In this paper, we propose SimProcess, a novel framework to rank the fidelity of ICS simulations by evaluating how closely they resemble real-world and noisy physical processes. It measures the simulation distance from a target system by estimating the noise distribution with machine learning models like Random Forest. Un- like existing solutions that require detailed mathematical models or are limited to simple systems, SimProcess operates with only a timeseries of measurements from the real system, making it applicable to a broader range of complex dynamic systems. We demonstrate the framework’s effectiveness through a case study using real-world power grid data from the EPIC testbed. We compare the performance of various simulation methods, including static and generative noise techniques. Our model correctly classifies real samples with a recall of up to 1.0. It also identifies Gaussian and Gaussian Mixture as the best distribution to simulate our power systems, together with a generative solution provided by an autoencoder, thereby helping developers to improve honeypot fidelity. Additionally, we make our code, dataset, and experimental results publicly available to foster research and collaboration.

Id prodotto:

145745

Handle IRIS:

11562/1161927

ultima modifica:

12 maggio 2025

Citazione bibliografica:

Donadel, Denis; Crestanello, Gabriele; Morandini, Giulio; Antonioli, Daniele; Conti, Mauro; Merro, Massimo, SimProcess: High Fidelity Simulation of Noisy ICS Physical Processes  in Proceedings of the 11th ACM Cyber-Physical System Security Workshop , ACM Press , Atti di "11th ACM Cyber-Physical System Security Workshop" , Ha Noi, Vietnam , 26 August 2025 , 2025 , pp. 1-12

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