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Related papers: A Security Framework for Chemical Functions

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Interconnected systems such as power systems and chemical processes are often required to satisfy safety properties in the presence of faults and attacks. Verifying safety of these systems, however, is computationally challenging due to…

Systems and Control · Electrical Eng. & Systems 2024-02-15 Luyao Niu , Abdullah Al Maruf , Andrew Clark , J. Sukarno Mertoguno , Radha Poovendran

Physical Unclonable Functions (PUFs) leverage inherent, non-clonable physical randomness to generate unique input-output pairs, serving as secure fingerprints for cryptographic protocols like authentication. Quantum PUFs (QPUFs) extend this…

The deployment of autonomous systems that operate in unstructured environments necessitates algorithms to verify their safety. This can be challenging due to, e.g., black-box components in the control software, or undermodelled dynamics…

Systems and Control · Electrical Eng. & Systems 2020-06-17 John Jackson , Luca Laurenti , Eric Frew , Morteza Lahijanian

Deploying safety-critical controllers in practice necessitates the ability to modulate uncertainties in control systems. In this context, robust control barrier functions -- in a variety of forms -- have been used to obtain safety…

Systems and Control · Electrical Eng. & Systems 2023-03-22 Anil Alan , Tamas G. Molnar , Aaron D. Ames , Gábor Orosz

Security has become a main concern for the smart grid to move from research and development to industry. The concept of security has usually referred to resistance to threats by an active or passive attacker. However, since smart meters…

Cryptography and Security · Computer Science 2023-07-25 Masoud Kaveh , Mohammad Reza Mosavi , Diego Martin , Saeed Aghapour

We introduce the first formal large-scale assessment of the utility of traditional chemical functional groups as used in chemical explanations. Our assessment employs a fundamental principle from computational learning theory: a good…

Machine Learning · Computer Science 2025-11-11 Ruben Sharma , Ross D. King

The success of Deep Neural Networks (DNNs) highly depends on data quality. Moreover, predictive uncertainty makes high performing DNNs risky for real-world deployment. In this paper, we aim to address these two issues by proposing a unified…

Machine Learning · Computer Science 2020-09-28 Krishanu Sarker , Xiulong Yang , Yang Li , Saeid Belkasim , Shihao Ji

The increasing cybersecurity threats to critical manufacturing infrastructure necessitate proactive strategies for vulnerability identification, classification, and assessment. Traditional approaches, which define vulnerabilities as…

Cryptography and Security · Computer Science 2025-06-06 Md Habibor Rahman , Mohammed Shafae

Robust control barrier functions (CBFs) provide a principled mechanism for smooth safety enforcement under worst-case disturbances. However, existing approaches typically rely on explicit, closed-form structure in the dynamics (e.g.,…

Systems and Control · Electrical Eng. & Systems 2026-04-16 Donggeon David Oh , Duy P. Nguyen , Haimin Hu , Jaime Fernández Fisac

In this paper, we present a new semi-decidable procedure to analyze cryptographic protocols for secrecy based on a new class of functions that we call: the Witness-Functions. A Witness-Function is a reliable function that guarantees the…

Cryptography and Security · Computer Science 2015-07-30 Jaouhar Fattahi , Mohamed Mejri , Hanane Houmani

Security-critical system requirements are increasingly enforced through mandatory access control systems. These systems are controlled by security policies, highly sensitive system components, which emphasizes the paramount importance of…

Cryptography and Security · Computer Science 2017-06-13 Peter Amthor

Artificial Chemistries (ACs) are symbolic chemical metaphors for the exploration of Artificial Life, with specific focus on the problem of biogenesis or the origin of life. This paper presents authors thoughts towards defining a unified…

Artificial Intelligence · Computer Science 2009-01-06 Janrdan Misra

Control Barrier Functions (CBFs) are a practical approach for designing safety-critical controllers, but constructing them for arbitrary nonlinear dynamical systems remains a challenge. Recent efforts have explored learning-based methods,…

Systems and Control · Electrical Eng. & Systems 2025-05-20 Manan Tayal , Aditya Singh , Pushpak Jagtap , Shishir Kolathaya

In recent years, a number of process-based anomaly detection schemes for Industrial Control Systems were proposed. In this work, we provide the first systematic analysis of such schemes, and introduce a taxonomy of properties that are…

Cryptography and Security · Computer Science 2023-06-27 Alessandro Erba , Nils Ole Tippenhauer

Cyber-Physical Systems (CPS) in domains such as manufacturing and energy distribution generate complex time series data crucial for Prognostics and Health Management (PHM). While Deep Learning (DL) methods have demonstrated strong…

Machine Learning · Computer Science 2025-12-16 Alexander Windmann , Henrik Steude , Daniel Boschmann , Oliver Niggemann

We propose a new formal criterion for secure compilation, providing strong security guarantees for components written in unsafe, low-level languages with C-style undefined behavior. Our criterion goes beyond recent proposals, which protect…

Noise on quantum devices is much more complex than it is commonly given credit. Far from usual models of decoherence, nearly all quantum devices are plagued both by a continuum of environments and temporal instabilities. These induce noisy…

Quantum Physics · Physics 2025-08-27 Gregory A. L. White , Petar Jurcevic , Charles D. Hill , Kavan Modi

Safety-critical control is a crucial aspect of modern systems, and Control Barrier Functions (CBFs) have gained popularity as the framework of choice for ensuring safety. However, implementing a CBF requires exact knowledge of the true…

Systems and Control · Electrical Eng. & Systems 2025-08-26 Rahal Nanayakkara , Aaron D. Ames , Paulo Tabuada

Uncertainty quantification enables users to assess the reliability of responses generated by large language models (LLMs). We present a novel Question Rephrasing technique to evaluate the input uncertainty of LLMs, which refers to the…

Computation and Language · Computer Science 2024-08-08 Zizhang Chen , Pengyu Hong , Sandeep Madireddy