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October 5, 2025Open Access

Detection and Identification of Sensor Attacks Using Data

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Data-driven methods identify malicious attacks in a sparse observability context, highlighting notable findings.

The study successfully identifies and detects malicious false-data injections in compromised datasets.
Conditions and algorithms were derived, demonstrating efficacy in systems with sparse observability.
Numerical simulations on a three-inertia system validate the proposed attack detection framework.
The approach addresses both compromised data and conditions with partial cleanliness.

Shinohara et al. (2025) studied this question.

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October 5, 2025Open Access

View Full Paper

Ask AI

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Authors

Data-driven methods identify malicious attacks in a sparse observability context, highlighting notable findings.

The study successfully identifies and detects malicious false-data injections in compromised datasets.
Conditions and algorithms were derived, demonstrating efficacy in systems with sparse observability.
Numerical simulations on a three-inertia system validate the proposed attack detection framework.
The approach addresses both compromised data and conditions with partial cleanliness.

Shinohara et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark