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Deep Learning based Covert Attack Identification for Industrial Control Systems
This data-driven framework considers the temporal behavior of a generic physical system that extracts features from the time series of the sensor measurements that can be used for detecting covert attacks, distinguishing them from equipment faults, as well as localize the attack/fault.
Multi-Sensor Slope Change Detection
Observations are assumed to be initially normal random variables with known constant means and variances.
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