Abstract: Systems and methods for generating access entitlements to networked computing resources. Systems may be configured to: receive an input data set representing an entitlement request associated with a user identifier; generate an entitlement prediction associated with the user identifier based on an entitlement model and at least one hierarchical level, the entitlement model defining a cluster representation of entitlement similarity, and wherein the entitlement prediction is based on one or more similarity relationships corresponding to the at least one hierarchical level; and transmit a signal representing the entitlement prediction for granting downstream access to a networked computing resource.

Abstract: Methods, systems, and techniques for detecting a cybersecurity breach. The cybersecurity breach may be a synthetic account or an account having been subjected to an account takeover. Electronic account data representative of accounts is obtained in which a first group of the accounts includes accounts flagged as being associated with the breach, and a second group of the accounts includes a remainder of the accounts. The computer system generates from the account data nodes representing the accounts and edges based on account metadata that connect the nodes. The computer system determines, such as by applying a link analysis method to the nodes and edges, a ranking of the accounts of at least part of the second group indicative of a likelihood that those accounts are also associated with the cybersecurity breach. That ranking may be used to identify which of those accounts is also identified with the cybersecurity breach.

Abstract: Salient features are extracted from a training data set. The training data set includes, for each of a subset of known legitimate websites and a subset of known phishing websites, Uniform Resource Locators (URLs) and Hypertext Markup Language (HTML) information. The salient features are fed to a machine learning engine, a classifier engine to identify potential phishing websites is generated by applying the machine learning engine to the salient features, and parameters of the classifier engine are tuned. This enables identification of potential phishing websites by parsing a target website into URL information and HTML information, and identifying predetermined URL features and predetermined HTML features. A prediction as to whether the target website is a phishing website or a legitimate website, based on the predetermined URL features and the predetermined HTML features, is received from the classifier engine.

Abstract: Protein-based nanoparticles and methods of forming such protein-based nanoparticles via electrohydrodynamic jetting methods are provided. The nanoparticle may comprise a water-soluble protein having an average molecular weight of ? about 8 kDa and < about 700 kDa. In certain variations, the water-soluble protein is cross-linked (e.g., with an optional crosslinking agent) and defines a mesh structure having an average linear mesh size of ? about 1 nm to ? about 4 nm. Methods of making such nanoparticles may include jetting a liquid comprising the water-soluble protein through a nozzle, followed by exposing the liquid to an electric field sufficient to solidify the liquid and form the protein-based nanoparticles described above.

Abstract: A computer system for, and method of, detecting fraudulent electronic transactions is provided. The system comprises at least one processor and a memory storing instructions which when executed by the processor configure the processor to perform the method. The method comprises accessing a trained model, receiving real-time transaction data, extracting graph-based and statistical features to enrich the real-time transaction data, and determining an account proximity score for the real-time transaction data.

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 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 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.