Abstract: A computer implemented system for electronic verification of credentials including at least one processor and data storage is described in various embodiments. The system includes cryptographic mechanisms and electronic communication between one or more computing systems that in concert, provide verification of a prover's credentials in accordance to logical conditions of a verifier's policy without providing additional information to a verifier entity.

Abstract: A system for reinforcement learning in a dynamic resource environment includes at least one memory and at least one processor configured to provide an electronic resource environment comprising: a matching engine and the resource generating agent configured for: obtaining from a historical data processing task database a plurality of historical data processing tasks, each historical data processing task including respective task resource requirement data; for a historical data processing task of the plurality of historical data processing tasks, generating layers of data processing tasks wherein a first layer data processing task has an incremental variant in its resource requirement data relative to resource requirement data for a second layer data processing task; and providing the layers of data processing tasks for matching by the machine engine.

Abstract: Systems and methods of generating interpretive data associated with data sets. Embodiments of systems may be for adapting Grad-CAM methods for embedding networks. The system includes a processor and a memory. The memory stores processor-executable instructions that, when executed, configure the processor to: obtain a subject data set; generate a feature embedding based on the subject data set; determine an embedding gradient weight based on a prior-trained embedding network and the feature embedding associated with the subject data set, the prior-trained embedding network defined based on a plurality of embedding gradient weights respectively corresponding to a feature map generated based on a plurality of training samples, and wherein the embedding gradient weight is determined based on querying a feature space for the feature embedding associated with the subject data set; and generate signals for communicating interpretive data associated with the embedding gradient weight.

Abstract: A system and method for handling crypto-asset transactions includes: receiving from a payment processing system an electronic transaction request including: a payment token corresponding to a payment identifier associated with the customer account, and a transaction amount in a fiat currency; determining current price data corresponding to a first crypto-asset and a second crypto-asset associated with the customer account; associating the electronic transaction request with at least one data processing task for executing at least one crypto-asset transaction; and when at least one crypto-asset confidence condition is satisfied based on the current price data of at least one of the first crypto-asset or the second crypto-asset, generating signals for providing, via the payment processing system, an indication that the electronic transaction request is authorized without waiting for confirmation of execution of the at least one crypto-asset transaction in the respective distributed ledger.

Abstract: Transaction destinations are identified by identifying requests for a login page of a web server for a financial institution and determining a referring website for each of the requests; classifying the referring websites into classes, each of the classes having a risk rating; identifying logins to access the web server and determining a user associated with each login; associating each of the logins with one of the requests and the referring website for that request; for each of the users, identifying transactions occurring within a time period from when the login was initiated; for each of the transactions occurring within the time period, associating a transaction destination of that transaction with the referring website for that login; and assigning a risk rating to each of the transaction destinations based at least in part on a risk rating of the class of the associated referring website.

Abstract: Systems for dynamically updating a priority queue for scheduling data operations associated with data records. A method includes: detecting event data meeting a threshold value for identifying a shift in data operation relevance and traversing a cascading data structure to identify an auditable entity corresponding to one or more data records associated with the event data, the one or more data records corresponding to at least one branch of the cascading data structure. The method includes generating an updated priority queue for scheduling data operations based on priority weights associated with branches of the cascading data structure, the updated priority queue based on event data corresponding to the identified auditable entity relative to event data associated with one or more records corresponding to other auditable entities and transmitting a signal for dynamically communicating the updated priority queue for scheduling data operations associated with data records of respective auditable entities.

Abstract: An authorization access system and method of minimizing unauthorized access to a resource are provided. The authorization access system comprises at least one processor, and a memory storing instructions which when executed by the at least one processor configure the at least one processor to perform the method. The method comprises assigning a first risk score to application programming interface (API) traffic associated with a user device and/or user behaviour pattern observed prior to an API gateway, assigning a second risk score to the API traffic associated with the user device observed at the API gateway, assigning a third risk score to the API traffic associated with the user device and/or back end service responses observed after the API gateway, and performing an authorization action based on any of the first, second or third risk scores.

Abstract: A system, electronic device and method for improved neural network training are provided. The electronic device includes: a processor, a memory storing a Generative adversarial network (GAN) to learn from unlabeled data by engaging a generative model in an adversarial game with a discriminator; and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for training the Generative adversarial network using a regularizer to encourage the discriminator to properly use its capacity and hidden representations of the discriminator to have high entropy.

Abstract: A system for detecting phishing events is provided. A data receiver is configured to receive datasets representative of web traffic associated with access to or on-going usage of an application hosted on a server of a production environment by a user. Bait user credentials are generated and inserted into a malicious online resource. When the bait user credentials are used to access a secure online resource, a malicious interaction fingerprint dataset is generated for similarity analysis and classification.

Abstract: A computer-implemented platform for adjudicating an electronic payment transaction between a user and a merchant is provided. The platform includes a computer system configured for the generation of a data structure containing payment data; a mobile device associated with a user configured to provide a tokenized data set to the computer system; and a merchant or processing terminal configured to communicate a merchant transaction data set, and the computer system using at least one of the tokenized data set and merchant transaction data set to authorize the transaction and generate a transaction confirmation data set. Corresponding methods and computer-readable media are also provided.

Abstract: Systems and methods for database access monitoring are provided. The system comprises at least one processor and a memory storing instructions which when executed by the at least one processor configure the at least one processor to perform the method. The method comprises receiving login event data, generating a vector representation of a subject entity and a vector representation of an object entity associated with a login event in the login event data, determining a distance between the subject entity and the object entity, and determining an anomaly score for the subject entity and the object entity. The anomaly score based at least in part on the distance between the subject entity and object entity.

Abstract: A cybersecurity platform is described that processes collected data using a data model to identify and link anomalies and in order to identify generate security events and intrusions. The platform generates graph data structures using the security anomalies extended using additional data. The graph data structures represent links between nodes, the links being events, the nodes being machines and user accounts. The platform processes the graph data structures by combining similar nodes or grouping security events with common features to behaviour indicative of a single or multiple security events to identify chains of events which together represent an attack.

Abstract: A distributed memory data repository of connected data centres. The network load balances by routing requests to different data centres for processing. The solution design provides a blue print to implement a distributed memory data repository based defense system across multiple nodes with dynamic fail-over capabilities. The defense system runs independently on a single node, exclusively leveraging memory for data storage and implementing a communication channel to interact with other nodes.

Abstract: A method for developing a containerized application using a pipeline platform consisting of a plurality of stages with associated development tools, the method comprising the steps of: receiving application parameters and a check-in code for the containerized application; generating a configuration file based on the application parameters, the configuration file containing configuration content including insert code; embedding the insert code into the check-in code; dynamically provisioning an opinionated pipeline based on contents of the configuration file, the opinionated pipeline including the plurality of stages with the associated development tools; setting up one or more control gates in one or more of the plurality of stages; receiving customized code for the containerized application, the customized code representing modifications of the insert code; and packaging the containerized application to include code contents of the check-in code, the customized code, and the insert code; wherein the containe

Abstract: Variational Autoencoders (VAEs) have been shown to be effective in modeling complex data distributions. Conventional VAEs operate with fully-observed data during training. However, learning a VAE model from partially-observed data is still a problem. A modified VAE framework is proposed that can learn from partially-observed data conditioned on the fully-observed mask. A model described in various embodiments is capable of learning a proper proposal distribution based on the missing data. The framework is evaluated for both high-dimensional multimodal data and low dimensional tabular data.

Abstract: Websites, having associated features, are clustered by filtering entries that may be legitimate, determining feature similarity scores between the website features, and generating an aggregated similarity matrix containing website similarity scores between the websites. Websites are clustered into clusters or groups, based in part on the aggregated similarity matrix. Each cluster is identified by a cluster identifier and represents a centroid website and other websites at a normalized similarity score from the centroid. It is determined for each website whether the normalized similarity score is less than a threshold, and if so is identified as weakly-similar. Above the threshold, the website is labelled with the cluster identifier. Further clustering and thresholding is performed on the weakly-similar websites into additional clusters.

Abstract: Effectively training machine learning systems with incomplete/partial labels is a practical, technical problem that solutions described herein attempt to overcome. In particular, an approach to modify loss functions on a proportionality basis is noted in some embodiments. In other embodiments, a graph neural network is provided to help identify correlations/causations as between categories. In another set of embodiments, a prediction approach is described to, based on originally provided labels, predict labels for unlabelled training samples such that the proportion of labelled labels relative to all labels is increased.

Abstract: A computer implemented system for electronic verification of credentials including at least one processor and data storage is described in various embodiments. The system includes cryptographic mechanisms and electronic communication between one or more computing systems that in concert, provide verification of a prover's credentials in accordance to logical conditions of a verifier's policy without providing additional information to a verifier entity.

Abstract: Systems and methods for monitoring suspicious communication network traffic. The methods include obtaining data associated with a sequence of communication events transmitted via the communication network and determining an entropy approximation measure associated at least one event attribute for the sequence of communication events. The method includes generating a threat prediction value based on an anomaly classification model and the entropy approximation measure. The anomaly classification model is trained based on prior sequences of communication events to identify a non-outlier anomaly range associated with the at least one event attribute. The threat prediction value is generated based on classification of the entropy approximation measure relative to the non-outlier anomaly range associated with the at least one attribute for identifying a potential threat. The method includes transmitting a signal for communicating that the sequence is a potential threat within the communication network.