Abstract: Pairing of an external device using a random user action is disclosed herein. An example method includes restricting the external device from accessing a resource. A user input receivable from the external device is identified based on a type of the external device, the user input not included in a list of previously generated user actions. In response to receipt of the user input from the external device within a threshold time period, the external device is authorized to access the resource.

Abstract: This disclosure relates generally to systems and methods for autonomous task composition of vision pipelines using an algorithm selection framework. The framework leverages transformer architecture along with deep reinforcement learning techniques to search an algorithmic space for unseen solution templates. In an embodiment, the present disclosure describes a two stage process of identifying the vision pipeline for a particular task. At first stage, a high-level sequence of the vision pipeline is provided by a symbolic planner to create the vision workflow. At second stage, suitable algorithms for each high-level task are selected. This is achieved by performing a graph search using a transformer architecture over an algorithmic space on each component of generated workflow. In order to make the system more robust, weights of embedding, key and query networks of a visual transformer are updated with a Deep Reinforcement Learning framework that uses Proximal Policy Optimization (PPO) as underlying algorithm.

Abstract: Conventional task planners assume that the task-plans provided are executable, hence these are not task-aware. Present disclosure alleviates the downward refinability assumption, that is, planning can be decomposed separate symbolic and continuous planning steps by introducing bi-level planning, a plan which is a series of actions that the robot needs to take to achieve the goal task is curated. Firstly, abstract symbolic actions are converted to continuous vectors and used therein to enable interaction with an environment. Images of objects placed in the environment are captured and concepts are learnt from the captured images and attributes of objects are detected. A hierarchical scene graph is generated from the concepts and attributes wherein the graph includes interpretable sub-symbolic representations and from these interpretable symbolic representations are obtained for identifying goal task.

Abstract: In some implementations there may be provided a system. The system may include a processor and a memory. The memory may include program code which causes operations when executed by the processor. The operations may include analyzing a series of events contained in received data. The series of events may include events that occur during the execution of a data object. The series of events may be analyzed to at least extract, from the series of events, subsequences of events. A machine learning model may determine a classification for the received data. The machine learning model may classify the received data based at least on whether the subsequences of events are malicious. The classification indicative of whether the received data is malicious may be provided. Related methods and articles of manufacture, including computer program products, are also disclosed.

Abstract: In one respect, there is provided a system for training a neural network adapted for classifying one or more scripts. The system may include at least one processor and at least one memory. The memory may include program code that provides operations when executed by the at least one processor. The operations may include: reducing a dimensionality of a plurality of features representative of a file set; determining, based at least on a reduced dimensional representation of the file set, a distance between a file and the file set; and determining, based at least on the distance between the file and the file set, a classification for the file. Related methods and articles of manufacture, including computer program products, are also provided.

Abstract: Particular embodiments described herein provide for an electronic device that can be configured to identifying a digital certificate associated with data and assigning a reputation to the digital certificate, where the digital certificate is classified as trusted if the digital certificate is included in an entry in a whitelist and the digital certificate is classified as untrusted if the digital certificate is included in an entry in a blacklist.

Abstract: In some implementations there may be provided a system. The system may include a processor and a memory. The memory may include program code which causes operations when executed by the processor. The operations may include analyzing a series of events contained in received data. The series of events may include events that occur during the execution of a data object. The series of events may be analyzed to at least extract, from the series of events, subsequences of events. A machine learning model may determine a classification for the received data. The machine learning model may classify the received data based at least on whether the subsequences of events are malicious. The classification indicative of whether the received data is malicious may be provided. Related methods and articles of manufacture, including computer program products, are also disclosed.

Abstract: Techniques and solutions are described for facilitating the conversion of tables, having primary data and an append specifying a format of append data, from a first format to a second format. A runtime object and a data dictionary entry can be created for a primary table in the second format, and at least a portion of primary data transferred to the primary table in the second format. A database layer object corresponding to the first table in the first format can be associated with a temporary runtime object and a temporary data dictionary entry so that append data can be transferred to an append table in the second format. Operations directed to the primary table in the second format can be modified to also be directed to the append table, such as using a JOIN view.

Abstract: Particular embodiments described herein provide for an electronic device that can be configured to monitor access to data in a secured area of memory at a hypervisor level, receive a request from a process to the data in the secured area, and deny the request if the process is not a trusted process. In an example, the electronic device is a point of sale device.

Abstract: In some implementations there may be provided a system. The system may include a processor and a memory. The memory may include program code which causes operations when executed by the processor. The operations may include analyzing a series of events contained in received data. The series of events may include events that occur during the execution of a data object. The series of events may be analyzed to at least extract, from the series of events, subsequences of events. A machine learning model may determine a classification for the received data. The machine learning model may classify the received data based at least on whether the subsequences of events are malicious. The classification indicative of whether the received data is malicious may be provided. Related methods and articles of manufacture, including computer program products, are also disclosed.

Abstract: Particular embodiments described herein provide for an electronic device that can be configured to receive a request from a process to access data is a system, determine if the data is in a virtualized protected area of memory in the system, and allow access to the data if the data is in the virtualized protected area of memory and the process is a trusted process. The electronic device can also be configured to determine if new data should be protected, store the new data in the virtualized protected area of memory in the system if the new data should be protected, and store the new data in an unprotected area of memory in the system if the new data should not be protected.

Abstract: In one respect, there is provided a system for training a neural network adapted for classifying one or more scripts. The system may include at least one processor and at least one memory. The memory may include program code that provides operations when executed by the at least one processor. The operations may include: reducing a dimensionality of a plurality of features representative of a file set; determining, based at least on a reduced dimensional representation of the file set, a distance between a file and the file set; and determining, based at least on the distance between the file and the file set, a classification for the file. Related methods and articles of manufacture, including computer program products, are also provided.

Abstract: In one respect, there is provided a system for training a neural network adapted for classifying one or more scripts. The system may include at least one processor and at least one memory. The memory may include program code that provides operations when executed by the at least one processor. The operations may include: reducing a dimensionality of a plurality of features representative of a file set; determining, based at least on a reduced dimensional representation of the file set, a distance between a file and the file set; and determining, based at least on the distance between the file and the file set, a classification for the file. Related methods and articles of manufacture, including computer program products, are also provided.

Abstract: Techniques and solutions are described for facilitating the conversion of tables, having primary data and an append specifying a format of append data, from a first format to a second format. A runtime object and a data dictionary entry can be created for a primary table in the second format, and at least a portion of primary data transferred to the primary table in the second format. A database layer object corresponding to the first table in the first format can be associated with a temporary runtime object and a temporary data dictionary entry so that append data can be transferred to an append table in the second format. Operations directed to the primary table in the second format can be modified to also be directed to the append table, such as using a JOIN view.

Abstract: In some implementations there may be provided a system. The system may include a processor and a memory. The memory may include program code which causes operations when executed by the processor. The operations may include analyzing a series of events contained in received data. The series of events may include events that occur during the execution of a data object. The series of events may be analyzed to at least extract, from the series of events, subsequences of events. A machine learning model may determine a classification for the received data. The machine learning model may classify the received data based at least on whether the subsequences of events are malicious. The classification indicative of whether the received data is malicious may be provided. Related methods and articles of manufacture, including computer program products, are also disclosed.

Abstract: Particular embodiments described herein provide for an electronic device that can be configured to receive a request from a process to access data is a system, determine if the data is in a virtualized protected area of memory in the system, and allow access to the data if the data is in the virtualized protected area of memory and the process is a trusted process. The electronic device can also be configured to determine if new data should be protected, store the new data in the virtualized protected area of memory in the system if the new data should be protected, and store the new data in an unprotected area of memory in the system if the new data should not be protected.

Abstract: Particular embodiments described herein provide for an electronic device that can be configured to receive untrusted input data at an enclave in an electronic device, isolate the untrusted input data from at least a portion of the enclave, communicate at least a portion of the untrusted data to an integrity verification module using an attestation channel, and receive data integrity verification of the untrusted input data from the integrity verification module. The integrity verification module can perform data integrity attestation functions to verify the untrusted data and the data integrity attestation functions include a data attestation policy and a whitelist.

Abstract: Particular embodiments described herein provide for an electronic device that can be configured to receive a request from a process to access data is a system, determine if the data is in a virtualized protected area of memory in the system, and allow access to the data if the data is in the virtualized protected area of memory and the process is a trusted process. The electronic device can also be configured to determine if new data should be protected, store the new data in the virtualized protected area of memory in the system if the new data should be protected, and store the new data in an unprotected area of memory in the system if the new data should not be protected.

Abstract: A method for backing up and restoring search service applications (SSAs) is provided that backs up an SSA topology extensible markup language (XML) file to a backup system. The method then backs up an SSA search topology XML file to the backup system and backs up SSA databases to the backup system. An application programming interface (API) is queried to discover internet information services (IIS) virtual directories associated with the SSA. Discovered IIS virtual directories associated with the SSA are backed up to the backup system IIS configuration data, IIS metabase data and registry data for the SSA are also backed up to the backup system. A similar process is utilized to restore this data to enable the recovery of an SSA.

Abstract: In some implementations there may be provided a system. The system may include a processor and a memory. The memory may include program code which causes operations when executed by the processor. The operations may include analyzing a series of events contained in received data. The series of events may include events that occur during the execution of a data object. The series of events may be analyzed to at least extract, from the series of events, subsequences of events. A machine learning model may determine a classification for the received data. The machine learning model may classify the received data based at least on whether the subsequences of events are malicious. The classification indicative of whether the received data is malicious may be provided. Related methods and articles of manufacture, including computer program products, are also disclosed.