Abstract: Embodiments of the present disclosure relate to scheduling maintenance services for a special-purpose vehicle. An example method for scheduling a service for a special-purpose vehicle includes: obtaining usage information for the special-purpose vehicle of a user, determining, based on the usage information, that the special-purpose vehicle is due for vehicle maintenance, providing, to a computing device of a service provider, a request to schedule the vehicle maintenance for the special-purpose vehicle according to an available service method type for the vehicle maintenance, receiving, from the computing device of the service provider, an indication that the service has been scheduled, and providing, in response to the received indication the service has been scheduled, an indication to a device of the user that the vehicle maintenance has been scheduled for the special-purpose vehicle.

Abstract: Antibody molecule-drug conjugates (ADCs) that specifically bind to lipopolysaccharides (LPS) are disclosed. The antibody molecule-drug conjugates can be used to treat, prevent, and/or diagnose bacterial infections and related disorders.

Abstract: Embodiments of the present disclosure relate scheduling maintenance services for a special-purpose vehicle. In an exemplary embodiment, a user device comprises a user interface, a processor, and memory comprising instructions that when executed by the processor cause the processor to output, to the user interface, a plurality of service method icons. The plurality of service methods comprise a first service method icon corresponding to a service being performed on the special-purpose vehicle at a first location and a second service method icon corresponding to the service being performed on the special-purpose vehicle at a second location. The first location and the second location are different locations. The memory also comprises instructions that when executed by the processor cause the processor to receive, via the user interface, a selection of a service method icon of the plurality of service method icons and send, to a scheduling server, a signal corresponding to the selected service method icon.

Abstract: Systems and methods include receiving a trained machine learning model that has been processed with training information removed therefrom, wherein the training information is utilized in training of the trained machine learning model; monitoring traffic, inline at the node, including processing the traffic with the trained machine learning model; obtaining a verdict on the traffic based on the trained machine learning model; and performing an action on the traffic based on the verdict.

Abstract: Systems and methods of sandboxing a file include responsive to receiving a file associated with a user, obtaining policy for the user; analyzing the file with a machine learning model; and based on a combination of the policy for the user and a verdict of the machine learning model, one of quarantining the file for analysis in a sandbox and allowing the file to the user. The present disclosure presents a smart quarantine with a goal of minimizing the number of files quarantined, the number of malicious files passed through to an end user, and a number of files scanned by a sandbox.

Abstract: Systems and methods include receiving network transaction data for a plurality of users monitored by a cloud-based system; creating a relationship graph based on the plurality of user's recent network transactions for a time period, wherein the relationship graph includes vertices for domains and edges for transactions by users between the domains having some number of transaction in the time period; and analyzing the relationship graph to detect previously undetected suspicious anomalies. The weights on each edge are based on a relationship between two domains where the relationship includes any of malware, Internet Protocol (IP) addresses, Autonomous System Number (ASN), registration, and redirects.

Abstract: Systems and methods include obtaining file identifiers associated with files in production data; obtaining lab data from one or more public repositories of malware samples based on the file identifiers for the production data; and utilizing the lab data for training a machine learning process for classifying malware in the production data. The obtaining file identifiers can be based on monitoring of users associated with the files, and only the file identifiers are maintained based on the monitoring. The lab data can include samples from the one or more public repositories matching the corresponding file identifiers for the production data. The lab data can include samples from the one or more public repositories that have features closely related to features of the production data.

Abstract: Systems and methods include obtaining a file associated with a user for processing; utilizing a combination of policy for the user and machine learning to determine whether to i) quarantine the file and scan the file in a sandbox, ii) allow the file to the user and scan the file in the sandbox, and iii) allow the file to the user without the scan; responsive to the quarantine of the file and the sandbox determining the file is malicious, blocking the file; and, responsive to the quarantine of the file and the sandbox determining the file is benign, allowing the file.

Abstract: Systems and methods include receiving a trained machine learning model that has been processed with training information removed therefrom, wherein the training information is utilized in training of the trained machine learning model; monitoring traffic, inline at the node, including processing the traffic with the trained machine learning model; obtaining a verdict on the traffic based on the trained machine learning model; and performing an action on the traffic based on the verdict.

Abstract: Systems and methods include training a machine learning model with data for identifying features in monitored traffic in a network; analyzing the trained machine learning model to identify information overhead therein, wherein the information overhead is utilized in part for the training; removing the information overhead in the machine learning model; and providing the machine learning model for runtime use for identifying the features in the monitored traffic, with the removed information overhead from the machine learning model.

Abstract: Systems and methods include utilizing a grouping model to identify a function of a user of a tenant; utilizing one or more behavior models to identify normal behavior and abnormal behavior of the user based on the function; and utilizing an orchestration model with a plurality of rules to score one or more of current and historical behavior of the user, based on the one or more behavior models; and utilizing an active learning model to improve the efficiency of the orchestration model The systems and methods can further include causing a security technique based on the score. The systems and methods can further include providing feedback based on the score to the one or more behavior models.

Abstract: Systems and methods include obtaining file identifiers associated with files in production data; obtaining lab data from one or more public repositories of malware samples based on the file identifiers for the production data; and utilizing the lab data for training a machine learning process for classifying malware in the production data. The obtaining file identifiers can be based on monitoring of users associated with the files, and only the file identifiers are maintained based on the monitoring. The lab data can include samples from the one or more public repositories matching the corresponding file identifiers for the production data. The lab data can include samples from the one or more public repositories that have features closely related to features of the production data.

Abstract: Embodiments of the present disclosure relate scheduling maintenance services for a special-purpose vehicle. In an exemplary embodiment, a user device comprises a user interface, a processor, and memory comprising instructions that when executed by the processor cause the processor to output, to the user interface, a plurality of service method icons. The plurality of service methods comprise a first service method icon corresponding to a service being performed on the special-purpose vehicle at a first location and a second service method icon corresponding to the service being performed on the special-purpose vehicle at a second location. The first location and the second location are different locations. The memory also comprises instructions that when executed by the processor cause the processor to receive, via the user interface, a selection of a service method icon of the plurality of service method icons and send, to a scheduling server, a signal corresponding to the selected service method icon.

Abstract: Systems and methods include training a machine learning model with data for identifying features in monitored traffic in a network; analyzing the trained machine learning model to identify information overhead therein, wherein the information overhead is utilized in part for the training; removing the information overhead in the machine learning model; and providing the machine learning model for runtime use for identifying the features in the monitored traffic, with the removed information overhead from the machine learning model.

Abstract: Systems and methods include obtaining a file associated with a user for processing; utilizing a combination of policy for the user and machine learning to determine whether to i) quarantine the file and scan the file in a sandbox, ii) allow the file to the user and scan the file in the sandbox, and iii) allow the file to the user without the scan; responsive to the quarantine of the file and the sandbox determining the file is malicious, blocking the file; and, responsive to the quarantine of the file and the sandbox determining the file is benign, allowing the file.