Abstract: In network security systems, graph-based techniques can be used to analyze data collected for a particular security incident, e.g., a command-and-control incident. In example embodiments, data extracted from data records of network activity and/or security alerts is used to generate a multipartite graph in which different entities (e.g., machines, processes, and domains or IP addresses) are represented as different types of nodes and relationships between the entities are represented as edges. The multipartite graph may be clustered, and the clusters be ranked according to some indicator of maliciousness (e.g., the number of associated security alerts or indicators of compromise (IoCs)). An output generated from the highest-ranking cluster(s) may serve, e.g., to identify new IoCs, or flow into mitigating actions taken in response to the incident.

Abstract: A design of an infrastructure deployment is used for new deployments or as part of an addition to an existing deployment. Because of the complexity of modern infrastructure deployment designs, these designs are subject to a number of problems that are too complex and extensive for manual detection of potential issues. Furthermore, these infrastructure deployments are often critical infrastructure, which means their timely deployment and proper functioning are necessary. While improper design can cause functional problems, selection of an infrastructure element that suffers a supply chain delay so as to delay deployment can be as negatively impactful as a poor design. Accordingly, embodiments herein help automate the analysis of an infrastructure deployment design. In one or more embodiments, a trained neural network receives as input a design and analyzes it to classify a particular issue or issues of the design.

Abstract: The current invention pertains to a computer-implemented method, a method, a use thereof, a computer system and a computer program product for determining a potential for anomalous activity by a suspect user, particularly for detecting fraud and most particularly for detecting money laundering.

Abstract: In network security systems, graph-based techniques can be used to analyze data collected for a particular security incident, e.g., a command-and-control incident. In example embodiments, data extracted from data records of network activity and/or security alerts is used to generate a multipartite graph in which different entities (e.g., machines, processes, and domains or IP addresses) are represented as different types of nodes and relationships between the entities are represented as edges. The multipartite graph may be clustered, and the clusters be ranked according to some indicator of maliciousness (e.g., the number of associated security alerts or indicators of compromise (IoCs)). An output generated from the highest-ranking cluster(s) may serve, e.g., to identify new IoCs, or flow into mitigating actions taken in response to the incident.

Abstract: A vehicle device may receive audio data and other vehicle data associated with a vehicle and may transform the audio data to transformed audio data in a frequency domain. The vehicle device may segment the transformed audio data into a plurality of audio segments and may process the plurality of audio segments, with different feature extraction techniques, to extract a plurality of feature vectors. The vehicle device may merge the plurality of feature vectors into a merged feature vector and may create an audio signature for the audio data based on the merged feature vector. The vehicle device may process the audio signature and the other vehicle data, with a model, to determine a classification of the audio signature and may perform one or more actions based on the classification of the audio signature.

Abstract: Hybrid deep learning systems and methods allow for detecting anomalies in objects, such as electrical printed circuit board (PCB) components, based on image data. In one or more embodiments, a hybrid deep learning model comprises a Graph Attention Network (GAT) that uses spatial properties of the PCB components to extract latent semantic information and generate an output set of hidden representations. The GAT treats each of the electrical components as a node and each connection between them as edges in a graph. The hybrid system further comprises a Convolutional Neural Network (CNN) that uses pixel data to obtain its own output set of hidden representations. The hybrid deep learning model concatenates both sets to detect anomalies that may be present on the PCB.

Abstract: Presented herein are embodiments that use a language model to embed or encode configuration elements (e.g., commands, prompts, etc.) into dense, latent representations that incorporate semantic and contextual information. Using a trained language model, a configuration for a network device may be converted into a set of configuration path sentences. Given a first set of encoded configuration path sentences for a first configuration and a second set of encoded configuration path sentences for a second configuration, these two sets may be compared to gauge a degree of difference between the two sets. In one or more embodiments, an Optimal Transport method with Wasserstein distance metric may be used to obtain a comparison value that gauges difference between the two configurations. In one or more embodiments, the comparison valuation may be labeled or classified by comparing the comparison value to one or more pre-defined thresholds.

Abstract: A vehicle device may receive audio data and other vehicle data associated with a vehicle and may transform the audio data to transformed audio data in a frequency domain. The vehicle device may segment the transformed audio data into a plurality of audio segments and may process the plurality of audio segments, with different feature extraction techniques, to extract a plurality of feature vectors. The vehicle device may merge the plurality of feature vectors into a merged feature vector and may create an audio signature for the audio data based on the merged feature vector. The vehicle device may process the audio signature and the other vehicle data, with a model, to determine a classification of the audio signature and may perform one or more actions based on the classification of the audio signature.

Abstract: In some implementations, a device may receive historical telematics data associated with a vehicle. The device may determine a set of base line driving parameters based on the historical telematics data. The device may receive current telematics data associated with the vehicle. The device may determine a set of current driving parameters based on the current telematics data. The device may determine, based on processing the set of base line driving parameters and the set of current driving parameters with a machine learning model, a type of driving behavior of a driver of the vehicle. The device may determine, based on processing the set of base line driving parameters and the set of current driving parameters with another machine learning model, a severity score associated with the type of driving behavior. The device may perform an action based on the type of driving behavior and the severity score.

Abstract: The current invention pertains to a computer-implemented method, a method, a use thereof, a computer system and a computer program product for determining a potential for anomalous activity by a suspect user, particularly for detecting fraud and most particularly for detecting money laundering.

Abstract: An in-vehicle device may receive an alert that includes first vehicle identifying a first vehicle and first location data identifying a first location, and may determine whether a second location of the in-vehicle device is in a geographic area associated with the first location. The in-vehicle device may cause, when the second location is determined to be in the geographic area associated with the first location, images or video to be captured of second vehicles, and may process the captured images or video, with a machine learning model, to determine second vehicle data in real time relative to capturing the images or video. The in-vehicle device may determine whether the second vehicle data substantially includes the first vehicle data, and may provide a notification to a device when the second vehicle data substantially includes the first vehicle data.

Abstract: A device can receive sensor data from a plurality of sensor devices. The sensor data can include information relating to vibrations detected by the plurality of sensor devices. The device can determine, based on the information relating to vibrations, whether the vibrations are likely to be associated with one or more primary waves of a seismic event. The device can predict, based on determining that the vibrations are likely to be associated with the one or more primary waves, a geographic area that is likely to be impacted by one or more secondary waves of the seismic event. The device can transmit, based on predicting the geographic area, and to one or more of a plurality of user equipments, one or more of a notification providing information relating to the seismic event or instructions to perform one or more actions.

Abstract: The present disclosure relates to compounds of Formula I, its stereoisomers, pharmaceutically acceptable salts, complexes, hydrates, solvates, tautomers, polymorphs, racemic mixtures, optically active forms thereof and pharmaceutical compositions containing them as the active ingredient which can be used as medicaments. The aforementioned substances can also be used in the manufacture of medicaments for treatment, prevention or suppression of diseases, and conditions mediated by microbes. The present disclosure also relates to the synthesis and characterization of aforementioned substances.

Abstract: The present invention relates to triaminopyrimidines and to pharmaceutically acceptable salts thereof, to their use in the treatment and/or prevention of malaria caused by plasmodium species, and to their methods of preparation.