Abstract:
The disclosed embodiments generally relate to detecting malware through detection of micro-architectural changes (morphing events) when executing a code at a hardware level (e.g., CPU). An exemplary embodiment relates to a computer system having: a memory circuitry comprising an executable code; a central processing unit (CPU) in communication with the memory circuitry and configured to execute the code; a performance monitoring unit (PMU) associated with the CPU, the PMU configured to detect and count one or more morphing events associated with execution of the code and to determine if the counted number of morphine events exceed a threshold value; and a co-processor configured to initiate a memory scan of the memory circuitry to identify a malware in the code.
Abstract:
System and techniques for multiple stream tuning are described herein. A plurality of content streams may be received. The plurality of content streams may be ranked. A user attention level may be scored as the user observes at least one content stream of the plurality of content streams. The user attention level and a rank for the at least one content stream may be compared to remaining ranks of the plurality of content streams to produce a difference. A stream action may be performed on a set of content streams from the plurality of content streams based on the difference.
Abstract:
Measurement exchange networks and protocols to exchange measurements of a parameter amongst devices (e.g., IoT devices), select the best measurement(s), accuracy/precision-wise, and determine a process variable for a control system based on the selected best measurement(s). A device may select a peer-provided best measurement to output as the process variable in place of a local measurement, and/or compute the process variable from multiple best measurements (e.g., local and/or peer-provided measurements). Metadata may be used to select a measurement(s) and/or to increase reliability/trust of exchanged data. In this way, each device of an exchange group/network may obtain the highest measurement accuracy of all available collocated sensors with little or no additional processing or cloud connectivity. A best measurement(s) may be selected based on measurement quality specifications extracted from metadata, measurement qualities computed from measurements of respective sensors, locations/proximities of the sensors, a policy(ies), and/or device IDs (e.g., extracted from metadata).
Abstract:
Technologies for secure collective authorization include multiple computing devices in communication over a network. A computing device may perform a join protocol with a group leader to receive a group private key that is associated with an interface implemented by the computing device. The interface may be an instance of an object model implemented by the computing device or membership of the computing device in a subsystem. The computing device receives a request for attestation to the interface, selects the group private key for the interface, and sends an attestation in response to the request. Another computing device may receive the attestation and verify the attestation with a group public key corresponding to the group private key. The group private key may be an enhanced privacy identifier (EPID) private key, and the group public key may be an EPID public key. Other embodiments are described and claimed.
Abstract:
In one embodiment, a system includes a hardware processor having at least one core to execute instructions; and a logic to generate a group public key for a subnet having a plurality of computing devices and generate a plurality of group private credentials for the plurality of computing devices, provide the group public key to the plurality of computing devices and provide each of the group private credentials to one of the plurality of computing devices, to enable communication between the plurality of computing devices of the subnet without validation messaging with the system. Other embodiments are described and claimed.
Abstract:
Systems, devices, and techniques for network-enabled device provisioning are disclosed herein. In some embodiments, a network-enabled device may include: a storage device; listening logic to wirelessly receive a plurality of key fragments from a corresponding plurality of peer devices, to cause storage of the plurality of key fragments in the storage device, and to receive an encrypted provisioning message from a management device; key generation logic to generate a decryption key based on the plurality of key fragments stored in the storage device to decrypt the encrypted provisioning message, and to decrypt the encrypted provisioning message using the decryption key; and control logic to provision the network-enabled device in accordance with instructions included in the decrypted provisioning message. Other embodiments may be disclosed and/or claimed.
Abstract:
Methods, systems, apparatus and articles of manufacture are disclosed to secure devices. An example disclosed apparatus includes a platform detector to determine when the device is within a threshold proximity to a platform, a device locking manager to initiate a locking service for the device when within the threshold proximity, and a device tampering manager to initiate a tampering remedy in response to detecting an indication of tampering.
Abstract:
In an embodiment, a system includes a processor that includes private key decryption logic to decrypt an encrypted private key received from a consuming device to produce a private key, and symmetric key decryption logic to receive the private key from the private key decryption logic and to decrypt an encrypted symmetric key received from the consuming device using the private key. The system also includes a dynamic random access memory (DRAM) coupled to the processor. Other embodiments are described and claimed.
Abstract:
A system establishes secure communications between first and second electronic devices. The first device stores secured content to be accessed by second device based on identification information of the first device. The identification information of the first device may be manually input into the second device, and the second device may perform an initial pairing operation with the first device based on this manually entered information. The identification information stored from initial pairing may allow secure automatic pairing.
Abstract:
Machine learning fraud resiliency using perceptual descriptors is described. An example of a computer-readable storage medium includes instructions for accessing multiple examples in a training dataset for a classifier system; calculating one or more perceptual hashes for each of the examples; generating clusters of perceptual hashes for the multiple examples based on the calculation of the one or more perceptual hashes for each of the plurality of examples; obtaining an inference sample for classification by the classifier system; generating a first classification result for the inference sample utilizing a neural network classifier and generating a second classification result utilizing the generated clusters of perceptual hashes; comparing the first classification result with the second classification result; and, upon a determination that the first classification result does not match the second classification result, determining a suspicion of an adversarial attack.