Abstract: A blockchain based alias directory may be utilized. Encrypted lists of aliases may be stored on the blockchain and may be accessible to network computers and secure gateways. Embodiments are directed to secure gateways and user devices for accessing the alias directory stored in the blockchain during a financial transaction. The user device may be provided with a list of aliases from which a user may select a payment account. Upon selection the user may be redirected to an identity verification system of the associated payment network.

Abstract: A system and method for detecting embedded malware from a device including a receiver for receiving embedded binary image; a memory for encoding and storing the received embedded binary image; and one or more processors coupled to the receiver. The method includes extracting statistical features from the encoded embedded binary image; producing gridded data from the statistical features, using SV; inputting the gridded data to a machine learning (ML) trained to detect embedded malware from the gridded data; and determining whether the embedded binary image is benign or malware.

Abstract: A communication device may send a public key externally; receive a specific signal from a first external device; determine whether a radio field intensity of the received specific signal is equal to or greater than a threshold value; receive an authentication request from the first external device; in a case where it is determined that the radio field intensity is equal to or greater than the threshold value and the authentication request is received from the first external device, send an authentication response to the first external device; after the authentication response has been sent to the first external device, receive connection information from the first external device; and establish, by using the connection information, a wireless connection between the communication device and a second external device.

Abstract: A system for providing an application includes an interface and a processor. The interface is configured to receive an indication to provide an application to a device. The processor is configured to provide the application to the device. The application is configured to receive a request for credentialed information associated with a user from a requesting server; determine whether a stored credential satisfies the request for the credentialed information; and in response to a determination that the stored credential satisfies the request for the credentialed information: determine a response credential for responding to the request; determine that the user approves sharing the credentialed information indicated by the response credential; and provide the response credential to the requesting server.

Abstract: The present disclosure relates to a method and system for managing and securing a distributed ledger for a decentralized peer-to-peer (p2p) network. The method receives an encrypted block and a group key generated by at least one peer node on the p2p network, wherein each peer node is IoT device and determines a virtual device block in a device chain on verifying the unique device ID, and address of a corresponding event chain associated with the virtual device block. Further, the method generates a transaction ID for a new transaction using the unique ID of the virtual device block and determines a valid event block in the event chain associated with the virtual device block for storing the new transaction and associated transaction ID. Further, the method updates the distributed ledger with the valid event block upon verification by one or more peer IoT devices of the p2p network.

Abstract: Features of sample files that are known to be normal are extracted by random projection. The random projection values of the sample files are used as training data to generate one or more anomaly detection models. Features of a target file being inspected are extracted by generating a random projection value of the target file. The random projection value of the target file is input to an anomaly detection model to determine whether or not the target file has features that are novel relative to the sample files. The target file is declared to be an outlier when an anomaly detection model generates an inference that indicates that the target file has novel features.

Abstract: A method for execution in a storage network includes receiving a request pertaining to a data object, where the request is related to a specified contiguous data range within the data object and the data object is dispersed error encoded to produce a plurality of groups of encoded data slices. The method continues by converting the request into a plurality of partial requests, where each partial request of the plurality of partial requests includes at least one partial task and transmitting each partial request of the plurality of partial requests to a storage unit of a set of storage units. The method then continues by determining when a partial request response has been received for each partial request of the plurality of partial requests and when a partial request response has been received for each partial request of the plurality of partial requests, transmitting an indication that the request pertaining to the data object has been successfully executed.

Abstract: Runtime security threats are detected and analyzed for serverless functions developed for hybrid clouds or other cloud-based deployment environments. One or more serverless functions may be received and executed within a container instance executing in a controlled and monitored environment. The execution of the serverless functions is monitored, using a monitoring layer in the controlled environment to capture runtime data including container application context statistics, serverless function input and output data, and runtime parameter snapshots of the serverless functions. Execution data associated with the serverless functions may be analyzed and provided to various supervised and/or unsupervised machine-learning models configured to detect and analyze runtime security threats.

Abstract: Systems and methods include determining a plurality of features associated with executable files, wherein the plurality of features are each based on static properties in predefined structure of the executable files; obtaining training data that includes samples of benign executable files and malicious executable files; extracting the plurality of features from the training data; and utilizing the extracted plurality of features to train a machine learning model to detect malicious executable files.

Abstract: A method of communicating through a less secure messaging system provides an option of masking messages sent from a second party to a first party so that they are unreadable when received by the first party. For some embodiments, it may be that they are visible, but unreadable, other embodiments, not visible. In either case, an authorization may be provided by the first party to make the masked messages readable messages, for many embodiments until a session rule makes the readable message a masked message again.

Abstract: A method of establishing a blockchain. The method comprises receiving an activation message that comprises micro code, building an activation block of a blockchain by a block foundry implemented in a processor of an integrated circuit (IC), wherein the activation block comprises a hash of a genesis block of the block chain that is stored in a non-volatile portion of a memory of the IC and a data portion that comprises the micro code, providing a copy of the activation block of the blockchain by the block foundry to a blockchain application executing in the processor of the IC, storing the copy of the activation block by the blockchain application in the non-volatile portion of the memory of the IC, and transmitting a copy of the genesis block and a copy of the activation block by the blockchain application to a wireless communication service provider.

Abstract: A data processing system and a method are provided for recognizing a scanned biometric characteristic in the data processing system. The data processing system includes a biometric sensor, a rich execution environment (REE), and a secure element (SE). In one embodiment, during an enrollment operation, a random challenge is applied to scanned data to produce a biometric template that is stored. During subsequent validation operations, the SE determines if user data includes evidence of the random challenge before providing access to a secure application. Evidence of the random challenge indicates the user data was provided by the biometric sensor. In another embodiment, the sensor data is split between the REE and the SE and partially processed in the SE. The described embodiments prevent a replay attack from being conducted in communications between the REE and the SE.

Abstract: A method includes receiving a build request containing build step instructions from a user. The build step instructions specify a usage of containers within memory hardware for building an output container. The containers include at least one private container having private contents and/or at least one public container having public contents. The method also includes authenticating the user initiating the build request and determining whether the user is authorized to access the private containers. When the user is authenticated and authorized to access the private containers, the method includes obtaining the containers specified by the build step instructions from the memory hardware, executing the build step instructions to build the output container while using the received containers, and outputting the built output container.

Abstract: Systems, methods, and program products for managing digital production from one or more production devices with one or more sources providing inputs of production designs and/or production options are disclosed.

Abstract: A first instance of an event management system monitors event data corresponding to a first group of users in a cloud computing system. Using a first machine learning (ML) algorithm, the first instance detects an anomalous event from the event data. The first ML algorithm is trained using historical event data. A second instance of the event management system is created and a copy of the first ML algorithm is added to the second instance. The second instance monitors second event data corresponding to a second group of users in the cloud computing system. Using the copy of the first ML algorithm, the second instance detects a second anomalous event from the second event data.

Abstract: A method includes providing a data set to an artificial intelligence filter trained to detect sensitive data based on sensitive data rules and detect one or more sensitive data values in the data set. The one or more sensitive data values are replaced with one or more substitute values in the data set, and the data set is associated with a key value. The data set is sent with the one or more substitute values to a third-party service to obtain a result. The key value associated with the result is identified. The one or more sensitive data values associated with the one or more substitute values are determined based on the key value. The one or more substitute values are replaced with the one or more sensitive data values in combination with a portion of the result to create a modified result.

Abstract: A method of data reduction in a partially encrypted volume includes receiving data to be stored on a storage array, decrypting the data using a first encryption key to generate first decrypted data, and decrypting the data using a second encryption key to generate second decrypted data. The method further includes comparing, by a storage array controller, a first compressibility value of the first decrypted data to a second compressibility value of the second decrypted data. The method further includes storing the first decrypted data if the first compressibility value is greater than or equal to the second compressibility value. The method further includes storing the second decrypted data if the second compressibility value is greater than the first compressibility value.

Abstract: Disclosed herein are an apparatus and method for detecting a malicious script. The apparatus includes one or more processors and executable memory for storing at least one program executed by the one or more processors. The at least one program is configured to extract token-type features, each of which corresponds to a lexical unit, and tree-node-type features of an abstract syntax tree from an input script, to train two learning models to respectively learn two pieces of learning data that are generated in consideration of features extracted respectively from the token-type features and the node-type features as having the highest frequency, and to detect whether the script is a malicious script based on the result of ensemble-based malicious script detection performed for the script, which is acquired using an ensemble detection model generated from the two learning models.

Abstract: A method and system for creating a composite security rating from security characterization data of a third party computer system. The security characterization data is derived from externally observable characteristics of the third party computer system. Advantageously, the composite security score has a relatively high likelihood of corresponding to an internal audit score despite use of externally observable security characteristics. Also, the method and system may include use of multiple security characterizations all solely derived from externally observable characteristics of the third party computer system.

Abstract: The techniques herein are directed generally to a “zero-knowledge” data management network. Users are able to share verifiable proof of data and/or identity information, and businesses are able to request, consume, and act on the data—all without a data storage server or those businesses ever seeing or having access to the raw sensitive information (where server-stored data is viewable only by the intended recipients, which may even be selected after storage). In one embodiment, source data is encrypted with a source encryption key (e.g., source public key), with a rekeying key being an encrypting combination of a source decryption key (e.g., source private key) and a recipient's public key. Without being able to decrypt the data, the storage server can use the rekeying key to re-encrypt the source data with the recipient's public key, to then be decrypted only by the corresponding recipient using its private key, accordingly.