Abstract: A login macro to automatically log into a web application running on a server computing device is generated from a provided username, a provided password, and a provided network address of the web application. The login macro can be generated regardless of whether the web application is logged into at a starting web page at the provided network address or at a sign-in web page navigable from the starting web page. After the login macro has been generated, that usage the login macro successfully results in logging into the web application running on the server computing device can be verified.

Abstract: Embodiments of the disclosure provide systems and methods for executing a functional test on an application. According to one embodiment, a method for running a functional test on an application can comprise executing the function test on the application. The functional test can comprise a plurality of steps. The plurality of steps can comprise a set of related steps having one or more relationships between each other and a set of one or more independent steps having no relationships to other steps in the plurality of steps. The one or more relationships between the set of related steps can be deduced and an indication of the deduced one or more relationships between the set of related steps can be saved. The functional test can then be replayed on the application based on the saved indication of the deduced one or more relationships between the set of related steps.

Abstract: A first network device nonce is computed. The first network device nonce is based on a first network device secret. A Change Token Table message (CTTM) is sent to a second network device. The CTTM comprises the first network device nonce. A Change Token Table Ack Message (CTTAM) with a second network device nonce is received from the second network device. A new token for a tokenization table is computed based on the first network device secret, the second network device nonce, a prime number, and a key derivation function. The new token for the tokenization table is also computed by the second network device based on a second network device secret, the first network device nonce, the prime number, and the key derivation function.

Abstract: Examples herein involve detection of entities in unstructured data. Terms are extracted from unstructured data. Entities scores for the terms are calculated using information from a name probability source, a known entity database, and historical context information. The entity scores indicate a probability that the respective terms refer to entities. The presence of detected entities are indicated based on the entity scores.

Abstract: A service receives, from client computing devices of client networks, information regarding incoming network traffic addressed to dark Internet Protocol (IP) address spaces the of client networks. The service can predict a cyber attack based on the information received from the client computing devices of the client networks. The server computing device notifies the client computing device of each client network affected by the predicted cyber attack.

Abstract: An image of a user interface of a device is captured. A graphical object is identified in the image of the user interface. For example, a menu item or a text object is identified in the image of the user interface. An Artificial Intelligence (AI) process is used to determine a confidence score for the graphical object; the confidence score identifies a confidence of how likely a type of the graphical object can be identified. In response to the first confidence score not meeting a threshold, a zoomed in image of the graphical object is taken or a zoomed-out image of the graphical object is taken. The zoomed in image or the zoomed-out image is used to increase the confidence score to better identify the type of the graphical object.

Abstract: According to an example, key splitting may include utilizing a masked version of a master key that is masked by using a mask.

Abstract: A first network device nonce is computed. The first network device nonce is based on a first network device secret. A Change Token Table message (CTTM) is sent to a second network device. The CTTM comprises the first network device nonce. A Change Token Table Ack Message (CTTAM) with a second network device nonce is received from the second network device. A new token for a tokenization table is computed based on the first network device secret, the second network device nonce, a prime number, and a key derivation function. The new token for the tokenization table is also computed by the second network device based on a second network device secret, the first network device nonce, the prime number, and the key derivation function.

Abstract: A first message is received from a first communication device. The first message comprises an authentication token. For example, the authentication token may be a username/password. A determination is made if the first message also comprises a valid temporary password. The temporary password is used to prevent a Denial-of-Service (DOS) attack. In response to the first message comprising the valid temporary password, a determination is made if the authentication token is valid. In response to the authentication token being valid, the first message is responded to in a normal manner. If the first message does not contain the temporary password, the first message is handled based on a DOS message handling process.

Abstract: A request to install a first version of a software application is received. The first version of the software application is stored in a first blockchain in a first distributed blockchain ledger. In response to receiving the request to install the first version of the software application, the first version of the software application is validated by running a hash of the first blockchain. In response to validating the first version of the software application, the first version of the software application is installed from the blockchain to a device. The software application may also be validated after being installed to the device.

Abstract: Back end calls triggered by a user interaction with a client user interface may be identified. The user interaction may be correlated with a logic flow, and the logic flow may be associated with the back end calls. A supervised learning model may be trained using a labeled data set comprising the back end calls and their associated logic flow. Rules may be derived from the supervised learning model for classifying other back end calls. The rules may be outputted to a classifier that utilizes the rules to associate the other back end calls with the logic flow.

Abstract: According to examples, an apparatus may include a processor and a memory on which are stored machine-readable instructions that when executed by the processor, may cause the processor to receive information regarding a performance characteristic of an application during predetermined time periods. The processor may calculate a rate of change in the performance characteristic over the predetermined time periods. Based on a determination that the performance characteristic of the application has changed over the predetermined time periods, the processor may tune values of a set of parameters for the application based on the calculated rate of change in the performance characteristic.

Abstract: A technique includes accessing data representing a state of a given investigation of a potential security threat to a computer system by a security analyst. The state includes a result of a current investigative step of the investigation, and the analyst conducting the investigation uses an investigation graphical user interface (GUI). The technique includes applying machine learning that is trained on observed investigations to determine a recommendation to guide the analyst in a next investigative step for the given investigation. The technique includes communicating the recommendation through an output provided to the investigation GUI.

Abstract: Context information associated with a network node is determined. For example, a network node (e.g., a server) has indicated that an application on the network node appears to be having performance problems. The context information may be to gather data about the application, such as, load, number of users accessing the application, etc. The context information comprises one or more rules for dynamically configuring how data is collected on the node. For example, the rule may indicate what specific statistics should be gathered from the application in real-time. The context information is sent to the network node. Node information is received from the network node in response to sending the context information. The node information is collected based on the one or more rules for configuring how data is collected on the network node. The node information is organized and is displayed on a user interface to a user.

Abstract: An apparatus may include a processor that may be caused to access handholding information indicating a hand configuration in which the apparatus is being held and compare the handholding information with previously-stored handholding authentication data of an authentic user. The previously-stored handholding authentication data may represent an authentic configuration in which the apparatus is held by the authentic user. The processor may be caused to determine that the apparatus is being held by the authentic user based on the comparison and cause an authenticated action to occur based on the determination.

Abstract: A device context of a device is identified based on device data including a current state of the device, and a user context of a user is identified based on user data including user interaction with a user interface for the device. A current task that the user is performing on the device in relation to the current state of the device is identified, and knowledge regarding how the user is performing the current task using the user interface is extracted, using an artificial intelligence technique with respect to the identified device context and the identified user context. The extracted knowledge for the identified current task is stored within a knowledge base, and can be used to adapt the user interface for another user performing the current task on the device in the current state.

Abstract: A request to load an application into memory for execution is received. The application is stored in one or more blocks in a blockchain. The application is validated by running a blockchain hash. In response to validating the application by running the blockchain hash, the application is loaded from the blockchain into a memory. The application is then executed in the memory. This provides a secure method of loading an application into memory.

Abstract: A request to add a new block to a blockchain is received. Data associated with the new block is scanned to identify malware and/or an anomaly. In response to identifying the malware and/or the anomaly in the data associated with the new block, an action is taken. The action includes: rejecting the request to add the new block to the blockchain, or removing the malware/anomaly from the new block and adding the new block to the blockchain. In a second embodiment, a malware event is identified that identifies malware/an anomaly in a block in a blockchain. In response to the malware event, an action is taken. The action includes: consolidating the blockchain, bypassing the block in the blockchain, consolidating the blockchain and bypassing the block in the blockchain, and deleting an encryption key that was used to encrypt the associated data that comprises the malware and/or the anomaly.

Abstract: An endpoint device monitors loading of dynamically loaded libraries (DLLs) by a process, such as during execution of the process on the endpoint device. The endpoint device can generate an endpoint-independent representation of the DLLs upon exiting of the process. The endpoint device may generate a hash, such as a one-way hash, of the endpoint-independent representation. The endpoint device may transmits the process and the hash to a management device to detect in a machine learning manner whether loading of the DLLs during the execution of the process was anomalous.

Abstract: According to examples, an apparatus may include a processor that may generate a migration assessment for resources of a computer system. In particular, the apparatus may logically divide topological information to facilitate identification of a resource, components used by the resource, and dependencies. The system further enables users to specify user-defined migration parameters that specify the migration. For instance, the parameters may specify a cost associated with the migration of the component, a license model of the component, a security requirement of the component, a performance of the component, a customization of the component, or requirement of the component. Migration assessments and decisions may be stored to train machine-learned models. For instance, the model may assess whether a parameter will be satisfied by using a certain cloud service and whether substitutes have sufficiently satisfied dependencies based on observed migration assessments and actual migrations.