Abstract: A first sub-blockchain and a second sub-blockchain are retrieved. The sub-blockchains are predefined fragments of a blockchain that are intended to be used to build a larger blockchain. At least one of the first sub-blockchain and the second sub-blockchain was originally stored in a read-only computer memory or was originally stored in a barcode/RFID tag. A blockchain that comprises the first sub-blockchain and the second sub-blockchain is created. The blockchain is used to track inventory of a product.

Abstract: A computing device includes a processor and a machine-readable storage storing instructions. The instructions are executable by the processor to: receive an input string including sensitive data to be encrypted; identify a first portion and a second portion of the input string, the first portion comprising the sensitive data; select, from a plurality of hash functions, a hash function based on the second portion; and generate a hash value of the first portion using the selected hash function.

Abstract: A database having multiple data columns is disclosed. Whether metadata for the multiple data columns resides in multiple different metadata columns may be determined. If so, the metadata may be consolidated into a single consolidated metadata column.

Abstract: Testing software applications often requires a balancing of thoroughness versus the time and computing resources available to perform such tests. By performing a static analysis on candidate software source code and, from the static analysis, configuring a dynamic analysis component to execute the tests, allows for extraneous tests to be omitted. For example, performing certain vulnerability attacks on a function may be futile if the attack requires a string input but the function only accepts integers. By combining static and dynamic analysis, unnecessary tests may be omitted and the results of each analysis process correlated to identify actual vulnerabilities or falsely indicted vulnerabilities reported by one of the static or dynamic analysis component.

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 track traffic events and user interface (UI) events in parallel and record a first event of a hybrid test script. The first event may be recorded in a traffic mode to record one or more of the traffic events. The processor may transition from the traffic mode to a UI mode and, responsive to the transition from the traffic mode to the UI mode, may share session data between the traffic events and the UI events. Based on the session data, the processor may record a second event of the hybrid test script in the UI mode.

Abstract: An event is detected. The event is a designated event for capturing a snapshot of a blockchain where the blockchain is not replicated in a distributed ledger. For example, the event may be where a specific number of new blocks have been added to the blockchain. In response to detecting the event, a capstone block or a lock block is created. The capstone block or the lock block is added to the blockchain. A copy of the capstone block or the copy of the lock block sent to a trusted authority. The copy of the capstone block or the copy of the lock block is used to validate some or all the blockchain to detect if the blockchain has been changed. For example, validating the blockchain may be to detect if the blockchain has been tampered with or has become corrupted.

Abstract: A nonce is sent to a user communication device. Obfuscated biometric data is received from the user communication device. The obfuscated biometric data is generated by an obfuscation process that uses the nonce and first biometric data. The first biometric data is generated from a biometric of a user at the user communication device. For example, the first biometric data may be biometric data from a fingerprint scan. The obfuscation process is reversed by using the first nonce to produce the first biometric data. The first biometric data is compared to known biometric data of the user. In response to the first biometric data being within a threshold of the known biometric data, the user is authenticated.

Abstract: A security rule associated with an application is identified. This may be done continuously and verified using machine learning models to ensure that the environment characterized by the data has not changed. For example, a security rule may be which ports are open/closed on a firewall. In response to identifying the security rule associated with the application, a security test based on the security rule is generated. For example, the security test may be to test all the ports on the firewall to see which ports are open/closed. The security test against the application is executed to determine if the security rule has been implemented properly by the application.

Abstract: Sensitive information is identified. For example, the sensitive information may be a set of medical records. A request is received to send the sensitive information from a first domain to a second domain. For example, the request may be to send the sensitive information from a first corporation to a second corporation. The sensitive information is encrypted. The encrypted sensitive information comprises an authentication field. The authentication field identifies one or more authentication factors that are required to unencrypt the sensitive information. For example, the authentication field may indicate that a user is required to provide a username/password and a fingerprint scan to access the sensitive information. The encrypted sensitive information is sent to the second domain. The user in the second domain is required to authenticate using the one or more authentication factors to access the sensitive information.

Abstract: A system and method are provided that store electronic data describing events that have occurred in a computing system, index the electronic data to create indexed data records; and store the indexed data records in computer memory as part of a flat data structure.

Abstract: A computing device includes a processor and a machine-readable storage storing instructions. The instructions are executable by the processor to: receive an input string including sensitive data to be encrypted; identify a first portion and a second portion of the input string, the first portion comprising the sensitive data; select, from a plurality of hash functions, a hash function based on the second portion; and generate a hash value of the first portion using the selected hash function.

Abstract: Methods and apparatus are disclosed to automatically generate a software test script from video and/or user input. Example methods disclosed herein include generating a first set of images from a video comprising recorded testing of software. The example method also includes processing the first set of images to remove unnecessary images. The example method also includes comparing each image to one or more adjacent images to identify one or more user actions performed in each image. The example method also includes translating each identified user action and/or user input into one or more test script parameters to generate an automated test script to execute on the software.

Abstract: A request to send a file is received. A file type for the file is identified. For example, the file type may a Joint Photographic Experts Group (JPEG) file or a Portable Document Format (PDF) file. In response to receiving the request to send the file and identifying the file type for the file, a format of the file is reordered. Reordering the format of the file is based on an application and/or an application type that will be receiving the file. For example, the application may be an Adobe Acrobat® reader that is receiving a PDF file that has been reordered for more efficient reading.

Abstract: In some examples, push control may include generation of a learning-based decision model based on analysis of data associated with historical usage of an application. For a request for content associated with usage of the application, the learning-based decision model may be analyzed to determine a next request for additional content expected to occur after the request for content. Further, a push operation may be performed to push the additional content associated with the next request to a source of the request for content.

Abstract: A plurality of events associated with a communication device are received. The plurality of events relate to a plurality of changes of hardware, firmware, and/or software in the communication device. The plurality of events are stored as a plurality of blocks in a blockchain. One or more anomalies associated with the plurality of changes of the hardware, the firmware, and/or the software are identified. In response to identifying the one or more anomalies associated with the plurality of changes of the hardware, the firmware, and/or the software in the communication device, a software image of the communication device is received and added to the blockchain. A second embodiment discloses using this process for a hypervisor using virtual machines or containers.

Abstract: Techniques for Precision-Preserving Format-Preserving Encryption (PPFPE) to protect numeric values and strings with leading/trailing and special characters, while preserving their precision, both as a protected value (e.g., ciphertext) and when decrypted are provided. In one aspect, a plaintext string is parsed to detect a first and last sentinel characters, wherein any character(s) preceding the first sentinel character comprises a leading character(s) and any character(s) following the last sentinel character comprises a trailing character(s). Any leading character(s) preceding the first sentinel character and/or any trailing character(s) following the last sentinel character are passed through. A ciphertext string is generated by encrypting the first and last sentinel character using one or more subsets of a set of encryption characters; and encrypting the portion of the plaintext string between the first and last sentinel character using the set of encryption characters.

Abstract: Systems and methods of associating text with a graphical user interface (GUI) object are disclosed. Disclosed systems and methods include identifying a GUI object and a text string. A positional relationship between the GUI object and the text string is determined. Based on the positional relationship between the GUI object and the text string, a map with an indication associating the GUI object with the text string is updated.

Abstract: An image of a graphical user interface is captured. A process is started to record identification of a graphical object (e.g., a text field) in the image of the graphical user interface. Recording identification of the graphical object in the image of the graphical user interface is based on machine learning. An end to the process to record identification of the graphical object in the image of the graphical user interface is identified. A machine learned software object is updated. For example, the machine learned software object is updated with a location of the graphical object. The updated machine learned software object then is used to test the graphical object. For example, to automatically fill in the text field using a test script.

Abstract: A dynamic transaction associated with a user is identified. For example, the user logging into a server is identified as a dynamic transaction. The dynamic transaction associated with the user is stored in a record (e.g., stored as a block in a blockchain). A first hash from a user device associated with the user is received. The second hash is generated based on the dynamic transaction. A second hash is generated based on the record. The first hash is compared to the second hash. In response to the first hash matching the second hash, the user is authenticated by providing access to a computer resource. For example, the user may be granted access to a database.

Abstract: Examples relate to load testing. The examples disclosed herein enable obtaining lines of code that are recorded as an application is executed in a client computing device, the lines of code being recorded in chronological order of the execution; determining whether a dependency on at least one variable exists in individual lines of the lines of code; in response to determining that the dependency exists, storing the dependency in a data storage; identifying, from the lines of code, a line of code including a network call statement that calls a called variable; and eliminating a first subset of the lines of code based on the called variable and dependencies stored in the data storage, wherein a second subset of the lines of code that remain after the elimination comprises user-entered parameter data.