Abstract:
In one embodiment, a processor of a vehicle detects a difference between a physical characteristic of the vehicle predicted by a first machine learning-based model and a physical characteristic of the vehicle indicated by telemetry data generated by a sub-system of the vehicle. The processor forms a packet payload of an update packet indicative of the detected difference, based in part on a relevancy of the physical characteristic to the first machine learning-based model. The processor applies a synchronization strategy to the update packet, to synchronize the update packet with a second machine learning-based model executed by a receiver. The processor sends the update packet to the receiver via a network, to update the second machine learning-based model.
Abstract:
In one embodiment, a processor of a vehicle predicts a state of the vehicle using a behavioral model. The model is configured to predict the state based in part on one or more state variables that are available from one or more sub-systems of the vehicle and indicative of one or more physical characteristics of the vehicle. The processor computes a representation of a difference between the predicted state of the vehicle and a measured state of the vehicle indicated by one or more state variables available from the one or more sub-systems of the vehicle. The processor detects a malicious intrusion of the vehicle based on the computed representation of the difference between the predicted and measured states of the vehicle exceeding a defined threshold. The processor initiates performance of a mitigation action for the detected intrusion, in response to detecting the malicious intrusion of the vehicle.
Abstract:
In one embodiment, a service receives signal characteristic data indicative of characteristics of wireless signals received by one or more antennas located in a particular area. The service uses the received signal characteristic data as input to a Bayesian inference model to predict physical states of an object located in the particular area. A physical state of the object is indicative of at least one of: a mass, a velocity, an acceleration, a surface area, or a location of the object. The service updates the Bayesian inference model based in part on the predicted state of the object and a change in the received signal characteristic data and based in part by enforcing Newtonian motion dynamics on the predicted physical states.
Abstract:
In one example embodiment, a network-connected device provides or obtains one or more computer network communications protected by a key. The network-connected device determines a count of the one or more computer network communications according to one or more properties of the one or more computer network communications. Based on the count of the one or more computer network communications, the network-connected device computes an information entropy of the key. Based on the information entropy of the key, the network-connected device dynamically generates a predicted threat level of the key.
Abstract:
Information describing a rule to be applied to a traffic stream is received at an edge network device. The traffic stream is received at the edge network device. A schema is applied to the traffic stream at the edge network device. It is determined that a rule triggering condition has been met. The rule is applied to the traffic stream, at the edge network device, in response to the rule triggering condition having been met. At least one of determining that the rule triggering event has taken place or applying the rule is performed based on the applied schema.
Abstract:
In one example embodiment, a network-connected device provides or obtains one or more computer network communications protected by a key. The network-connected device determines a count of the one or more computer network communications according to one or more properties of the one or more computer network communications. Based on the count of the one or more computer network communications, the network-connected device computes an information entropy of the key. Based on the information entropy of the key, the network-connected device dynamically generates a predicted threat level of the key.
Abstract:
According to one or more embodiments of the disclosure, thing discovery and configuration for an Internet of Things (IoT) integrated developer environment (IDE) is shown and described. In particular, in one embodiment, a computer operates an IoT IDE that discovers real-world physical devices within a computer network that are available to participate with the IoT IDE. The IoT IDE may then determine a respective functionality of each of the real-world physical devices, and virtually represents the real-world physical devices as selectable options within the IoT IDE for an IoT application, where a respective virtual representation of each of the real-world physical devices is configured within the IoT IDE with the corresponding respective functionality of that real-world physical device. Simulating the IoT application within the IoT IDE then relays input and/or output (I/O) between the IoT IDE and a selected set of real-world physical devices according to their corresponding respective functionality.
Abstract:
In one embodiment, a stream of data packets originated by a visual data source is received at an edge device in a network. The data packets include at least one of video data, image data, and geo spatial data. Next, a visual data attribute is extracted at the edge device from the stream of data packets according to an edge-based extraction algorithm. The extracted visual data attribute is vectorized at the edge device via quantization vectors. The vectorized visual data attribute is then indexed at the edge device in a schema-less database that stores indexed visual data attributes.
Abstract:
A method of continuous multi-factor authentication may include executing wireless sensing based at least in part on execution of a continuous multi-factor authentication (CMFA) application at a computing device, collecting channel state information (CSI) data from a network device communicatively coupled to the computing device, transmitting the CSI data to a CMFA device, and receiving a trust score from the CMFA device based on the CSI data.
Abstract:
Techniques for using Network Address Translation (NAT), Mobile Internet Protocol (MIP), and/or other techniques in conjunction with Domain Name System (DNS) to anonymize server-side addresses in data communications. Rather than having DNS provide a client device with an IP address of an endpoint device, such as a server, the DNS instead returns a random IP address that is mapped to the client device and the endpoint device. In this way, IP addresses of servers are obfuscated by a random IP address that cannot be used to identify the endpoint device or service. The client device may then communicate data packets to the server using the random IP address as the destination address, and a gateway that works in conjunction with DNS can convert the random IP address to the actual IP address of the server using NAT and forward the data packet onto the server.