Described are methods, systems and computer readable media for data source refreshing.

A password evaluation engine used to evaluate a user's password that redefines the concepts of password complexity and password strength is discussed. Password complexity may be calculated by the evaluation engine so as to take into account the amount of knowledge possessed by a potential attacker, seeking to crack the password, of the rules corresponding to a rule set used for generating the password. A determination of password strength by the evaluation engine may consider a potential attacker's computational resources, the protection function used to protect/store a password and the amount of time available to the attacker to crack the password with respect to an identified search space based on the attacker's knowledge. Embodiments also enable a password strength estimator to be evaluated and policy recommendations to be generated for an entity's password policy requirements.

Enforcing shadow stack violations at module granularity, rather than at thread or process granularity. An exception is processed during execution of a thread based on code of an application binary, which is enabled for shadow stack enforcement, that calls an external module. The exception results from a mismatch between a return address popped from the thread's call stack and a return address popped from the thread's shadow stack. Processing the exception includes determining that the exception resulted from execution of an instruction in the external module, and determining whether or not the external module is enabled for shadow stack enforcement. Based at least on these determinations, execution of the thread is terminated when the external module is enabled for shadow stack enforcement, or the thread is permitted to continue executing when the external module is not enabled for shadow stack enforcement.

Automated malware detection for application file packages using machine learning (e.g., trained neural network-based classifiers) is described. A particular method includes generating, at a first device, a first feature vector based on occurrences of character n-grams corresponding to a first subset of files of multiple files of an application file package. The method includes generating, at the first device, a second feature vector based on occurrences of attributes in a second subset of files of the multiple files. The method includes sending the first feature vector and the second feature vector from the first device to a second device as inputs to a file classifier. The method includes receiving, at the first device from the second device, classification data associated with the application file package based on the first feature vector and the second feature vector. The classification data indicates whether the application file package includes malware.

In an embodiment, dynamically-generated code may be supported in the system by ensuring that the code either remains executing within a predefined region of memory or exits to one of a set of valid exit addresses. Software embodiments are described in which the dynamically-generated code is scanned prior to permitting execution of the dynamically-generated code to ensure that various criteria are met including exclusion of certain disallowed instructions and control of branch target addresses. Hardware embodiments are described in which the dynamically-generated code is permitted to executed but is monitored to ensure that the execution criteria are met.

The present disclosure is directed to for secure data access between multiple entities, and includes actions of receiving, by a secure file storage system, a set of metafiles including one or more metafiles that define actions to be performed and conditions to be satisfied before granting a first system use of data that is resident at a second system, the set of metafiles being provided by the second system, receiving, by the secure file storage system and from a central exchange, an indication that the actions are performed and the conditions are satisfied for use of the data by the first system, wherein the central exchange accesses the set of metafiles from the secure file storage without accessing the data, and in response to the indication, permitting use of the data by the first system.

The present technology relates to identifying an artificial intelligence model based on a received first key value to write a received first block of data associated with a file. The received first key value is applied to the identified artificial intelligence model which is trained to output one of a plurality of actual index values where the identified artificial intelligence model and the plurality of data blocks are stored as a neural tree. The one of the actual index values is compared to a range within the actual index values to determine when the one of the actual index value points to a first data block of the plurality of data. The received first block of data associated with the file is written into the determined first data block.

A building system for operating a building and managing private building information includes a processing circuit configured to receive a request for information for a building entity of a building entity database. The processing circuit is configured to select one of the mask templates from the entity database based on access values associated with the requesting device and a relational link between the building entity and the mask templates, retrieve private information for the building entity in response to a reception of the request for the information, and generate a masked information data structure based on the private information and the one of the mask templates.

Aspects of the invention include a computer-implemented method of executing a hybrid quantum safe key exchange system. The computer-implemented method includes initially retrieving an authenticated random value from a trusted source, generating a first Z value using a first elliptic curve (EC) private key and a first certified form of an EC public key with an EC Diffie-Hellman (ECDH) algorithm, deriving a shared key using the authenticated random value and the first Z value with a key derivation function, decrypting the authenticated random value using a quantum safe algorithm (QSA) private key, generating a second Z value using a second EC private key and a second certified form of the EC public key with the ECDH algorithm and deriving the shared key using the authenticated random value and the second Z value with the key derivation function.

Convenient sharing of information among authorized network users may be facilitated by allowing a user to send information originating from multiple applications in aggregate form to another user, e.g., using a secure messaging service. In scenarios where data access is restricted, a server may check the recipient's access privileges prior to forwarding the information to her.