Abstract: A service obtains traffic logs for traffic of a network that has been sent according to a Layer 7 protocol (e.g., SNMP or DNS). The service identifies from the traffic logs device names that appear to correspond to different devices/NICs as names of candidate multi-NIC devices. The service extracts features from names of the candidate multi-NIC devices and generates respective feature vectors. The service can generate “documents” representing each device name from which it extracts features by determining n-grams of each device name, where a set of n-grams of a device name is treated as a document, and each n-gram is treated as a term in the document. Exemplary features that can be extracted based on a device name document include within-document and cross-document uniqueness scores. The service clusters the feature vectors with unsupervised learning and identifies clusters of a size that satisfies a criterion as corresponding to multi-NIC devices.

Abstract: Data packets transmitted to and from an IoT device are obtained and at least one of the data packets are analyzed using deep packet inspection to identify transaction data from payload of the at least one of the data packets. An event log is generated for the IoT device from the transaction data, the event log, at least in part, used to generate a historical record for the IoT device. The IoT device into a device profile based on the historical record for the IoT device. The event log is updated in real-time to indicate current operation of the IoT device. Abnormal device behavior of the IoT device is determined using the event log and the device profile. The device profile is updated to indicate the abnormal device behavior of the IoT device.

Abstract: A high-temperature resistant peelable anti-corrosion coating, a preparation method therefor and an application thereof are provided. The coating includes component A and component B; the component A includes film-forming substance, pigment and filler, auxiliary agent, and solvent A; the component B includes vulcanizing agent and solvent B; the component A includes the following components in parts by weight: 100 parts of film-forming substance; 25-55 parts of pigment and filler; 0.1-3 parts of adhesion modifier; 0.5-5 parts of auxiliary agent; 20-50 parts of solvent A; the component B includes the following components in parts by weight: 10-50 parts of vulcanizing agent; 100 parts of solvent B. The coating can be completely peeled off from the substrate after being subjected to a high temperature of 200° C. for 4 hours, without affecting the state of the substrate.

Abstract: A software bill of materials (SBOM) and vulnerability management system (“system”) disclosed herein extracts software component-related identifiers for Internet of Things (IoT) devices using deep packet inspection. The system filters the identifiers by removing identifiers that match a blacklist of identifiers known to not correspond to software components. The system then populates SBOM fields using a database storing the filtered identifiers with a schema that is uniform across SBOM file formats and queries a vulnerability database with software components indicated in the filtered identifiers to identify vulnerabilities for each IoT device for security risk assessment.

Abstract: Techniques for detecting anomalous network behavior in operational technology (OT) protocols are disclosed. A system, process, and/or computer program product for detecting anomalous network behavior in OT protocols include monitoring network traffic to perform automated OT malware detection analysis of OT related network traffic, extracting one or more features from the OT related network traffic, inputting the one or more extracted features into a model for malware detection analysis, and performing an action based on a result of the model.

Abstract: A service obtains traffic logs for traffic of a network that has been sent according to a Layer 7 protocol (e.g., SNMP or DNS). The service identifies from the traffic logs device names that appear to correspond to different devices/NICs as names of candidate multi-NIC devices. The service extracts features from names of the candidate multi-NIC devices and generates respective feature vectors. The service can generate “documents” representing each device name from which it extracts features by determining n-grams of each device name, where a set of n-grams of a device name is treated as a document, and each n-gram is treated as a term in the document. Exemplary features that can be extracted based on a device name document include within-document and cross-document uniqueness scores. The service clusters the feature vectors with unsupervised learning and identifies clusters of a size that satisfies a criterion as corresponding to multi-NIC devices.

Abstract: An electrical connector assembly includes: a printed circuit board; an electrical connector seated upon the printed circuit board; an electronic package coupled to the electrical connector; a frame structure affixed to the printed circuit board; a metallic securing seat affixed to the frame structure and having plural securing posts; a heat sink positioned upon the electronic package and having plural through holes aligned with the securing posts; plural fasteners each extending through a corresponding through hole to engage a corresponding securing post and plural springs each compressed between an associated fastener and the heat sink; and a retention member mounted to a corresponding securing post for engaging the heat sink, wherein the retention member has a mounting part and a latching part pivoted to the mounting part.

Abstract: A folding device, a folding housing provided with the folding device, and an electronic device are provided. The folding device includes a support assembly, comprising a side support member on each of two opposite sides of the folding device; a rotating assembly, comprising a rotating member on each of two opposite sides of the folding device and a connecting member on each of two opposite sides of the folding device; and a linkage assembly, comprising a linkage member on each of two opposite sides of the folding device. The connecting member is rotatable to drive the rotating member and the linkage member to rotate, and rotations of the rotating member and the linkage member can drive the side support member to move, such that the two side support members on the two opposite sides of the folding device are configured to be synchronously folded or synchronously unfolded to each other.

Abstract: An ecological management method and system for tropical marine ranching, a device, and a medium are provided, which relates to fishery resource management technologies. The method includes: obtaining ecological environment data and energy flow analysis data of a target marine ranching; performing ecological evaluation on the ecological environment data to determine an ecological comprehensive evaluation result, the ecological comprehensive evaluation result includes a health condition evaluation and an ecosystem stability evaluation of the tropical marine ranching; performing, based on the energy flow analysis data and the ecological comprehensive evaluation result, bio-capacity evaluation by using an Ecopath energy flow model to obtain a current bio-capacity; and performing ecological management and developing an ecological management strategy based on the current bio-capacity, the ecological management strategy includes an enhancement and releasing scheme and a fishing scheme.

Abstract: Increasing use of web-based applications or Software-as-a-Service and IoT devices within enterprise networks increases the variety of network traffic and variables for consideration in managing security posture, which includes policy management. A security posture management system as disclosed herein leverages application identification and device discovery from ongoing collection and analysis of network traffic data to manage policies at device granularity allowing tailored security posture management. The system can tailor policies to handle network traffic depending on identified application and device type inputs obtained from the ongoing collection and analysis. The security posture management system can configure SD-WAN construct based parameters of a policy to tailor policies for different application traffic from different types of devices.

Abstract: A service obtains traffic logs for traffic of a network that has been sent according to a Layer 7 protocol (e.g., SNMP or DNS). The service identifies from the traffic logs device names that appear to correspond to different devices/NICs as names of candidate multi-NIC devices. The service extracts features from names of the candidate multi-NIC devices and generates respective feature vectors. The service can generate “documents” representing each device name from which it extracts features by determining n-grams of each device name, where a set of n-grams of a device name is treated as a document, and each n-gram is treated as a term in the document. Exemplary features that can be extracted based on a device name document include within-document and cross-document uniqueness scores. The service clusters the feature vectors with unsupervised learning and identifies clusters of a size that satisfies a criterion as corresponding to multi-NIC devices.

Abstract: Techniques for grouping and labeling Internet of Things (IoT) devices are disclosed. A first set of raw events associated with a first IoT device is identified, including a transmission made by the first IoT device. A communication manner of the first IoT device is determined, based at least in part on a communication manner of the first IoT device. The first set of raw events over the first time period is examined to generate one or more formatted events of the first IoT device. The formatted events are used to extract a set of features. Similar processing is performed with respect to a second IoT device. A context-based IoT device grouping model is generated based on at least one of: (1) the features extracted for the first IoT device or (2) the features extracted for the second IoT device. The model is applied to determine that a third IoT device belongs to a particular group. A deviation by the third IoT device from group behavior is detected and an alert is generated in response.

Abstract: A method for treating alkaline fluorine-containing wastewater by combining an induced crystallization method with a tubular membrane filter (TMF) includes: introducing fluorine-containing wastewater into a regulating pond to regulate to 8.0-8.

Abstract: A set of data packets transmitted by an IoT device is received at a system. At least one packet included in the set of data packets is analyzed. An Authentication, and Account (AAA) message, including contextual information associated with the IoT device, is transmitted on behalf of the IoT device.

Abstract: A first device governs operation of a second device based on a network security risk posed by the second device. The second device is disposed locally to the first device and in local network communication with the first device. The first device is in network communication with a cloud-based computational service.

Abstract: Techniques for grouping and labeling Internet of Things (IoT) devices are disclosed. A first set of raw events associated with a first IoT device is identified, including a transmission made by the first IoT device. A communication manner of the first IoT device is determined, based at least in part on a communication manner of the first IoT device. The first set of raw events over the first time period is examined to generate one or more formatted events of the first IoT device. The formatted events are used to extract a set of features. Similar processing is performed with respect to a second IoT device. A context-based IoT device grouping model is generated based on at least one of: (1) the features extracted for the first IoT device or (2) the features extracted for the second IoT device. The model is applied to determine that a third IoT device belongs to a particular group. A deviation by the third IoT device from group behavior is detected and an alert is generated in response.

Abstract: Techniques for grouping and labeling Internet of Things (IoT) devices are disclosed. A set of raw events associated with a first IoT device is identified. A context of the first IoT device is identified, and used to enrich at least some of the raw events. At least some of the raw events are aggregated. A context-based IoT device grouping model is generated based at least in part on the aggregated events and events associated with a second IoT device in operation. The model is applied to determine that a third IoT device belongs to a particular group. A deviation by the third IoT device from group behavior is detected and an alert is generated in response.

Abstract: The present disclosure provides a quantitative accuracy evaluation method and system for a result of remote sensing inversion. The method includes the following steps: acquiring measured data of a target element content in a preset zone; acquiring, based on acquired remote sensing image reflectivity of the preset zone, predicted data of the target element content in the preset zone by a remote sensing inversion model; and conducting comprehensive index evaluation of abnormal feature similarity between the measured data and the predicted data. The technical solution adopted in the present disclosure can solve the problem with quantitative accuracy evaluation of remote sensing inversion that inverted object content and measured object content are distributed inconsistently in space.

Abstract: Techniques for grouping and labeling Internet of Things (IoT) devices are disclosed. A set of raw events associated with a first IoT device is identified. A context of the first IoT device is identified, and used to enrich at least some of the raw events. At least some of the raw events are aggregated. A context-based IoT device grouping model is generated based at least in part on the aggregated events and events associated with a second IoT device in operation. The model is applied to determine that a third IoT device belongs to a particular group. A deviation by the third IoT device from group behavior is detected and an alert is generated in response.