Abstract: A method of identifying a target from synthetic aperture radar (SAR) data without incurring the computational load associated with generating an SAR image. The method includes receiving SAR data collected by a radar system including RF phase history data associated with reflected RF pulses from a target in a scene, but excluding an SAR image. Range profile data is determined from the SAR data by converting the RF phase history data into a structured temporal array that can be applied as input to a classifier incorporating a recurrent neural network, such as a recurrent neural network made up of long short-term memory (LSTM) cells that are configured to recognize temporal or spatial characteristics associated with a target, and provide an identification of a target based on the recognized temporal or spatial characteristic.

Abstract: A system for cyber-attack detection within cyber-physical systems (such as a vehicle). The system operates by obtaining a time-varying analog side-channel signal from components with the cyber-physical system and converting the time-varying analog side-channel signal to a digital side-channel signal. A time-series of system states are then identified based on the digital side-channel signal. The time-series of system states are compared with software states as generated by the cyber-physical system components, such that when the software states are unmatched with the time-series of system states, a side-channel is designated as having a security breach. In such an event, the cyber-physical system is then caused to implement an action based on the side-channel security breach.

Abstract: A method of identifying a target from synthetic aperture radar (SAR) data without incurring the computational load associated with generating an SAR image. The method includes receiving SAR data collected by a radar system including RF phase history data associated with reflected RF pulses from a target in a scene, but excluding an SAR image. Range profile data is determined from the SAR data by converting the RF phase history data into a structured temporal array that can be applied as input to a classifier incorporating a recurrent neural network, such as a recurrent neural network made up of long short-term memory (LSTM) cells that are configured to recognize temporal or spatial characteristics associated with a target, and provide an identification of a target based on the recognized temporal or spatial characteristic.

Abstract: A system for cyber-attack detection within cyber-physical systems (such as a vehicle). The system operates by obtaining a time-varying analog side-channel signal from components with the cyber-physical system and converting the time-varying analog side-channel signal to a digital side-channel signal. A time-series of system states are then identified based on the digital side-channel signal. The time-series of system states are compared with software states as generated by the cyber-physical system components, such that when the software states are unmatched with the time-series of system states, a side-channel is designated as having a security breach. In such an event, the cyber-physical system is then caused to implement an action based on the side-channel security breach.