11 results about "Network Endpoint" patented technology

Techniques described herein can efficiently detect network endpoints that are vulnerable to individual security threats. New security threats are constantly emerging. Identifying endpoints that are vulnerable to a security threat enables proactive protection of the vulnerable endpoints. Furthermore, detecting endpoints that are vulnerable to a security threat enables tailored protection operations that are limited to protecting vulnerable endpoints without necessarily also expending resources to protect invulnerable endpoints. Historic vulnerability data is used to group endpoints into cohorts that share similar histories of security infections and compromises. When an active security compromise is discovered at an affected endpoint, cohort protection operations can be applied to protect endpoints in the same cohort as the affected endpoint.

This invention belongs to the field of inspection data processing technology, specifically involving a drone inspection video stream communication method, gateway, and medium based on device status recognition. The inspection monitoring gateway acquires security configurations, verifies the initial data packets encrypted and forwarded by the drone via the inspection node, and establishes a secure connection. The real-time video stream is identified by device type and its status characteristics are analyzed, classifying the video stream into datasets of different priorities. Adaptive communication channels are constructed for each priority dataset based on device type and network endpoint information. Differentiated upload frequencies and strategies are implemented according to the priority of the datasets through the corresponding channels. The method achieves control from data security access and intelligent analysis to network resource scheduling. The raw video is converted into a priority-based structured data stream. By dynamically matching business priorities with network channels, highly reliable transmission of high-value alarm data is ensured, improving the security and real-time response capability of the inspection system.

This invention relates to the field of Internet of Things (IoT) technology, specifically to a data encryption method based on WiFi Mesh. The WiFi Mesh-based data encryption method includes the following steps: S1, a key exchange phase, where an unconnected node sends a network access request to a connected node, and the connected node generates a public key A and a private key a using an asymmetric encryption algorithm; S2, the connected node sends public key A to the unconnected node, which generates a random key B using a random number, and encrypts the random key B using public key A to obtain ciphertext b; the broadcast key E is initially generated by the WiFi root node using a random number, and all WiFi Mesh network endpoints use the same broadcast key E; the unicast key F is randomly generated by the connected node. Compared with existing technologies, this invention has the following advantages: it solves the problem of sensitive information leakage during the initial key exchange in data transmission using an asymmetric encryption algorithm; it improves the starting point of the entire cycle, ensuring the security of subsequent data communication.

Systems, switches, network endpoints, and methods are provided. In one example, a system is described that includes a latency measurement circuit to measure traffic on a network from an endpoint sender to an endpoint receiver across multiple paths. The system also includes a packet marking circuit to provide a routing mark for a packet destined for the endpoint receiver according to a network traffic measurement provided by the latency measurement circuit, where the routing mark provides an indication that supports routing for the packet to reach the endpoint receiver via a chosen path or subset of paths among the multiple paths.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for mitigating computing latency associated with proving of possession of network endpoints. One system includes (i) a user device, e.g., a smartphone or a personal computer, (ii) a proof of possession service running on one or more computers, and (iii) an authorization service running on one or more computers.

The present disclosure generally relates to offloading the orchestration and control of a communication collective to a specialized collective offload engine. The systems, methods, and specialized computing hardware described herein avoid the latencies and other inefficiencies introduced when software applications control the execution of a communication collective. For example, the described systems, methods, and specialized computing hardware generate a binary representation of one or more direct acyclic graphs of node operations for a communication collective, and load this representation into the instruction memories of specialized collective offload engines residing on network endpoints connected to one or more network switches. During execution of the collective, the collective offload engines initiate node operations based on whether corresponding dependencies are met. By offloading these tasks to the collective offload engines, no additional latencies are introduced and other computing resources are kept free for use by other applications.

This application provides a distributed computing power management system, a distributed computing power management method, and a computing device. It includes a registration node and multiple management nodes, including a first management node and at least one second management node. The first management node sends a registration request to the registration node. The registration node returns network endpoint information of currently registered second management nodes to the first management node and notifies the second management node of the network endpoint information of newly registered first management nodes. The first management node also establishes a communication connection with the second management node based on the network endpoint information of the second management node and synchronizes their respective total computing power resources with the established communication connection second management node. By constructing a decentralized distributed architecture, each management node can directly communicate with each other and share the total computing power resources after registration, achieving resource discovery and state synchronization, and improving the availability and robustness of the system.

Dynamic endpoint management is performed for heterogeneous machine learning models. Placement events for machine learning models associated with managed network endpoints are detected. These managed network endpoints can provide access to machine learning models via requests received from clients of the machine learning service to invoke different specified machine learning models. Computational resources are selected from associated computing resources based on a determination that the computing resources meet the resource requirements of the machine learning model, and the machine learning model is placed on the selected computing resource.

Endpoint security groups include computing device endpoints that are classified according to commonly shared device features and capabilities including device type, function, role, or location. Endpoint security groups are used as an alternative identity mechanism for endpoints for purposes of security and data traffic policy enforcement rather than using conventional IP (Internet Protocol) addressing. Grouping endpoints reduces the scope of network management to enable dynamic policy enforcement for endpoints as they join, leave, and then rejoin computing networks, which is a common behavior, particularly for IoT (Internet-of-Things) devices in manufacturing environments. In an illustrative example, a private multi-access edge compute (MEC) platform supports a scalable policy definition and enforcement framework that provides consistent endpoint handling independent of network access methodology. Endpoint security groups facilitate improvements in security of network access and utilization and segmentation of data traffic on a fine-grained basis.

Examples of the disclosure provide for a scatter network device. In some examples, the scatter network device includes a non-transitory memory, at least one processor, and a key exchange application stored in the non-transitory memory. When executed by the at least one processor, the key exchange application transmits a key exchange request to a first network endpoint, the key exchange request including an identifier of the scatter network device, receives a key exchange response from the first network endpoint, the key exchange response including a set of one-time-use endpoint validation tokens (EVTs) uniquely associated with the identifier of the scatter network device, and transmits anauthenticated message to a second network endpoint, the authenticated message including a first of the set of one-time-use EVTs concatenated with an encrypted data portion.

This application provides a method, apparatus, and electronic device for identifying mixed stormwater and sewage connections in drainage pipe networks. The method includes: acquiring real-time GIS data of the drainage pipe network, constructing an undirected graph model, and determining the graph node features and edge features in the model; in this model, manholes and pipe network endpoints are used as graph nodes, and pipe segment connection relationships are used as edges; preprocessing the graph node features; and inputting the edge features and the preprocessed graph node features into the mixed stormwater and sewage connection identification model so that the model outputs the mixed stormwater and sewage connection point identification results. The mixed stormwater and sewage connection identification model includes a GNN model suitable for mixed stormwater and sewage connection identification, constructed using an encoder-processor-decoder architecture of SWE-GNN and incorporating the flux exchange principle of shallow water equations. This application can integrate pipe network topological relationships, capture the dynamic evolution of mixed stormwater and sewage, and has strong generalization ability of the mixed stormwater and sewage connection identification model, thereby improving the accuracy and efficiency of mixed stormwater and sewage connection identification in drainage pipe networks.