Abstract: In various examples, systems and methods are disclosed herein for a vehicle command operation system that may use technology across multiple modalities to cause vehicular operations to be performed in response to determining a focal point based on a gaze of an occupant. The system may utilize sensors to receive first data indicative of an eye gaze of an occupant of the vehicle. The system may utilize sensors to receive second data indicative of other data from the occupant. The system may then calculate a gaze vector based on the data indicative of the eye gaze of the occupant. The system may determine a focal point based on the gaze vector. In response to determining the focal point, the system causes an operation to be performed in the vehicle based on the second data.

Abstract: A system for identifying one or more anomalous network sessions in a network environment includes a memory and a processor coupled to the memory. The processor collects network traffic data based on a plurality of network sessions between a source and a destination in the network environment. The processor parses the network traffic data to identify a plurality of parameters associated with the network traffic data at different network layers. The processor clusters the network traffic data into baseline network traffic data based on one or more specific parameters of the plurality of parameters. The processor identifies unknown network traffic based upon the clustered network traffic data and analyzes the unknown network traffic with respect to the baseline network traffic data to identify the one or more anomalous network sessions. The processor determines and performs one or more mitigation actions for the identified one or more anomalous network sessions.

Abstract: A system includes a computing infrastructure and an application prioritization system. The computing infrastructure includes a plurality of computing devices configured to implement computing applications. The application prioritization system receives application data associated with the computing applications. A request is received for a priority of a first computing application of the computing applications compared to a second computing application of the computing applications. The application prioritization system determines, using a feedback-based machine learning model, a first priority of the first computing application and a second priority of the second computing application and an explanation of the first and second priorities. A response is provided with an indication of the larger of the first priority and second priority.

Abstract: In various examples, systems and methods are disclosed herein for a vehicle command operation system that may use technology across multiple modalities to cause vehicular operations to be performed in response to determining a focal point based on a gaze of an occupant. The system may utilize sensors to receive first data indicative of an eye gaze of an occupant of the vehicle. The system may utilize sensors to receive second data indicative of other data from the occupant. The system may then calculate a gaze vector based on the data indicative of the eye gaze of the occupant. The system may determine a focal point based on the gaze vector. In response to determining the focal point, the system causes an operation to be performed in the vehicle based on the second data.

Abstract: A code repository stores source code. An insider threat detection system stores instructions for detecting code defects and criteria indicating predetermined types of code defects that, when present, are associated with intentional obfuscation of one or more functions of the source code. The insider threat detection system receives an entry of source code and detects, using the model, a set of code defects in the entry of source code. A defect type is determined for each code defect, thereby determining a set of defect types included in the entry of source code. If it is determined that each of the predetermined types of code defects indicated by the criteria is included in the determined set of defect types, the entry of source code is determined to include an insider threat.

Abstract: A system includes a computing infrastructure and an application prioritization system. The computing infrastructure includes a plurality of computing devices configured to implement computing applications. The application prioritization system receives application data associated with the computing applications. A request is received for a priority of a first computing application of the computing applications compared to a second computing application of the computing applications. The application prioritization system determines, using a feedback-based machine learning model, a first priority of the first computing application and a second priority of the second computing application and an explanation of the first and second priorities. A response is provided with an indication of the larger of the first priority and second priority and the explanation.

Abstract: A code repository stores source code. An insider threat detection system stores instructions for detecting code defects and criteria indicating predetermined types of code defects that, when present, are associated with intentional obfuscation of one or more functions of the source code. The insider threat detection system receives an entry of source code and detects, using the model, a set of code defects in the entry of source code. A defect type is determined for each code defect, thereby determining a set of defect types included in the entry of source code. If it is determined that each of the predetermined types of code defects indicated by the criteria is included in the determined set of defect types, the entry of source code is determined to include an insider threat.

Abstract: A system includes a database, a memory, and a processor. The database stores data associated with a known security threat. The memory includes a threat model associated with a software application. The processor identifies, based on natural language processing of the data associated with the known security threat, one or more attributes of software susceptible to the known security threat. The processor also identifies, based on natural language processing of the threat model, one or more attributes of the software application. The processor additionally determines, based on a comparison between the one or more attributes of software susceptible to the known security threat and the one or more attributes of the software application, that the software application is susceptible to the known security threat. In response, the processor updates the threat model to reflect the susceptibility of the software application to the known security threat.

Abstract: A resource management system receives a set of application priorities. The resource management system determines, based at least in part on the received set of application priorities, a resource allocation corresponding to a proposed distribution of the computing applications and the users amongst the computing devices of a computing infrastructure. The resource management system determines, using the resource allocation, a recommended device configuration for each of the computing devices. The resource management system automatically implements the determined resource allocation using the device configuration determined for each of the computing devices.

Abstract: A prioritization system includes a memory that stores an access record with, for each of the users, an indication of a previous usage of computing applications. The memory stores a permission record with, for each of the users, an indication of the computing applications that the user is permitted to access. The memory stores user affinities that include, for each of the users, an affinity score corresponding to a predetermined ability level of the user to engage in an activity associated with one or more of the computing applications. The prioritization system determines a priority score for each of the users. In response to receiving a request for a priority of a first user of the users, the prioritization system provides a response with the priority score determined for the first user of the users.

Abstract: In various examples, systems and methods are disclosed herein for a vehicle command operation system that may use technology across multiple modalities to cause vehicular operations to be performed in response to determining a focal point based on a gaze of an occupant. The system may utilize sensors to receive first data indicative of an eye gaze of an occupant of the vehicle. The system may utilize sensors to receive second data indicative of other data from the occupant. The system may then calculate a gaze vector based on the data indicative of the eye gaze of the occupant. The system may determine a focal point based on the gaze vector. In response to determining the focal point, the system causes an operation to be performed in the vehicle based on the second data.

Abstract: In various examples, systems and methods are disclosed herein for a vehicle command operation system that may use technology across multiple modalities to cause vehicular operations to be performed in response to determining a focal point based on a gaze of an occupant. The system may utilize sensors to receive first data indicative of an eye gaze of an occupant of the vehicle. The system may utilize sensors to receive second data indicative of other data from the occupant. The system may then calculate a gaze vector based on the data indicative of the eye gaze of the occupant. The system may determine a focal point based on the gaze vector. In response to determining the focal point, the system causes an operation to be performed in the vehicle based on the second data.

Abstract: A system includes a database, a memory, and a processor. The database stores data associated with a known security threat. The memory includes a threat model associated with a software application. The processor identifies, based on natural language processing of the data associated with the known security threat, one or more attributes of software susceptible to the known security threat. The processor also identifies, based on natural language processing of the threat model, one or more attributes of the software application. The processor additionally determines, based on a comparison between the one or more attributes of software susceptible to the known security threat and the one or more attributes of the software application, that the software application is susceptible to the known security threat. In response, the processor updates the threat model to reflect the susceptibility of the software application to the known security threat.

Abstract: Systems and methods are disclosed herein for a pedestrian crossing warning system that may use multi-modal technology to determine attributes of a person and provide a warning to the person in response to a calculated risk level to effect a reduction of the risk level. The system may utilize sensors to receive data indicative of a trajectory of a person external to the vehicle. Specific attributes of the person such as age or walking aids may be determined. Based on the trajectory data and the specific attributes, a risk level may be determined by the system using a machine learning model. The system may cause emission of a warning to the person in response to the risk level.

Abstract: Systems and methods are disclosed herein for implementation of a vehicle command operation system that may use multi-modal technology to authenticate an occupant of the vehicle to authorize a command and receive natural language commands for vehicular operations. The system may utilize sensors to receive data indicative of a voice command from an occupant of the vehicle. The system may receive second sensor data to aid in the determination of the corresponding vehicular operation in response to the received command. The system may retrieve authentication data for the occupants of the vehicle. The system authenticates the occupant to authorize a vehicular operation command using a neural network based on at least one of the first sensor data, the second sensor data, and the authentication data. Responsive to the authentication, the system may authorize the operation to be performed in the vehicle based on the vehicular operation command.

Abstract: A system configured for identifying unpermitted data in source code receives a search query comprising particular keywords related to the unpermitted data. The system labels the source code with vulnerability factors and categories of those vulnerability factors, where the vulnerability factors indicate a security vulnerability and the categories provide information about the security vulnerability of the source code. The system performs a static analysis on the source code to identify instances of the particular keyword in a data flow and control flow of the source code. The system performs a vulnerability analysis on the source code to determine a vulnerability level of the source code, in which factor weights and category weights for each code portion of the source code are determined. The system calculates a weighted sum of the factor weights and category weights for each code portion, thereby detecting instances of unpermitted data in source code.

Abstract: Systems and methods are disclosed herein for implementation of a vehicle command operation system that may use multi-modal technology to authenticate an occupant of the vehicle to authorize a command and receive natural language commands for vehicular operations. The system may utilize sensors to receive data indicative of a voice command from an occupant of the vehicle. The system may receive second sensor data to aid in the determination of the corresponding vehicular operation in response to the received command. The system may retrieve authentication data for the occupants of the vehicle. The system authenticates the occupant to authorize a vehicular operation command using a neural network based on at least one of the first sensor data, the second sensor data, and the authentication data. Responsive to the authentication, the system may authorize the operation to be performed in the vehicle based on the vehicular operation command.

Abstract: A code repository stores source code. An insider threat detection system stores instructions for detecting code defects and criteria indicating predetermined types of code defects that, when present, are associated with intentional obfuscation of one or more functions of the source code. The insider threat detection system receives an entry of source code and detects, using the model, a set of code defects in the entry of source code. A defect type is determined for each code defect, thereby determining a set of defect types included in the entry of source code. If it is determined that each of the predetermined types of code defects indicated by the criteria is included in the determined set of defect types, the entry of source code is determined to include an insider threat.

Abstract: A resource management system receives a set of application priorities. The resource management system determines, based at least in part on the received set of application priorities, a resource allocation corresponding to a proposed distribution of the computing applications and the users amongst the computing devices of a computing infrastructure. The resource management system determines, using the resource allocation, a recommended device configuration for each of the computing devices. The resource management system automatically implements the determined resource allocation using the device configuration determined for each of the computing devices.

Abstract: A prioritization system includes a memory that stores an access record with, for each of the users, an indication of a previous usage of computing applications. The memory stores a permission record with, for each of the users, an indication of the computing applications that the user is permitted to access. The memory stores user affinities that include, for each of the users, an affinity score corresponding to a predetermined ability level of the user to engage in an activity associated with one or more of the computing applications. The prioritization system determines a priority score for each of the users. In response to receiving a request for a priority of a first user of the users, the prioritization system provides a response with the priority score determined for the first user of the users.