Abstract: A multi-die device a first die containing a plurality of first die signal path elements configured to propagate a stimulus signal and a second die containing a plurality of second die signal path elements configured to propagate the stimulus signal. The multi-die device further includes an interposer configured to establish signal communication between the first die and the second die so as to deliver the stimulus signal from the plurality of first die signal path elements to the plurality of second die signal path elements to generate a propagation delay. The propagation delay is used to generate a single unified PUF response that is indicative of the authenticity of the multi-die device.

Abstract: A system and method for detecting embedded malware from a device including a receiver for receiving embedded binary image; a memory for encoding and storing the received embedded binary image; and one or more processors coupled to the receiver. The method includes extracting statistical features from the encoded embedded binary image; producing gridded data from the statistical features, using SV; inputting the gridded data to a machine learning (ML) trained to detect embedded malware from the gridded data; and determining whether the embedded binary image is benign or malware.

Abstract: A multi-die device a first die containing a plurality of first die signal path elements configured to propagate a stimulus signal and a second die containing a plurality of second die signal path elements configured to propagate the stimulus signal. The multi-die device further includes an interposer configured to establish signal communication between the first die and the second die so as to deliver the stimulus signal from the plurality of first die signal path elements to the plurality of second die signal path elements to generate a propagation delay. The propagation delay is used to generate a single unified PUF response that is indicative of the authenticity of the multi-die device.

Abstract: A system and method for detecting embedded malware from a device including a receiver for receiving embedded binary image; a memory for encoding and storing the received embedded binary image; and one or more processors coupled to the receiver. The method includes extracting statistical features from the encoded embedded binary image; producing gridded data from the statistical features, using SV; inputting the gridded data to a machine learning (ML) trained to detect embedded malware from the gridded data; and determining whether the embedded binary image is benign or malware.

Abstract: A method of generating cyber chaff can include determining a cell of a grid of cells to which a first feature and a second feature of user data maps, identifying a cell type of the cell, the cell type indicating whether the cell is an active cell, an inactive cell, or a sub-process cell, and providing cyber chaff based on cyber chaff data associated with either (a) one or more cells of the inactive cell type or (b) one or more cells of the sub-process cell type.

Abstract: A method, apparatus and computer-readable medium for testing a target device. A fuzzer and a monitor are connected to the target device via a tactical bus. The fuzzer records messages sent from a source device to the target device over the tactical bus, creates a first fuzzed message having a data structure of the recorded message, and sends the first fuzzed message to the target device over the tactical bus. A fuzzer monitor monitors the target device for an anomalous response to the first fuzzed message, and determines a vulnerability of the target device from the response to the first fuzzed message.

Abstract: A method of generating cyber chaff can include determining a cell of a grid of cells to which a first feature and a second feature of user data maps, identifying a cell type of the cell, the cell type indicating whether the cell is an active cell, an inactive cell, or a sub-process cell, and providing cyber chaff based on cyber chaff data associated with either (a) one or more cells of the inactive cell type or (b) one or more cells of the sub-process cell type.

Abstract: A method, apparatus and computer-readable medium for testing a target device. A fuzzer and a monitor are connected to the target device via a tactical bus. The fuzzer records messages sent from a source device to the target device over the tactical bus, creates a first fuzzed message having a data structure of the recorded message, and sends the first fuzzed message to the target device over the tactical bus. A fuzzer monitor monitors the target device for an anomalous response to the first fuzzed message, and determines a vulnerability of the target device from the response to the first fuzzed message.