Abstract: Serverless containerization of a collaboration application improves deployment and security of the collaboration application. A system may generate a container within a private cloud. The container may execute the collaboration application. The container is assigned an Internet Protocol (IP) address and is associated with a security group that controls network traffic to and from the container. The system may include a network load balancer configured with the IP address assigned to the container that allocates computational resources for executing the collaboration application. The system may include serverless resources such as a database and filesystem that is accessible to the container within the private cloud. The system may include a private cloud (PC) endpoint configured to establish a private connection with an end user interface, wherein the private connection provides secure network traffic between the PC endpoint the end user interface within a private network of the PC endpoint.

Abstract: A fraud prevention system that includes a fraud prevention server including an electronic processor and a memory. The memory includes an online application origination (OAO) service. When executing the OAO service, the electronic processor is configured to determine whether the OAO service is enabled and whether a website configuration includes a list of multi-page placements for an online application, determine that input data needs to be stored in the memory and combined into multi-page input data, determine a fraud risk score of the online application based on the multi-page input data and an online application origination (OAO) model that differentiates between a behavior of a normal user and a behavior of a nefarious actor during a submission of the online application on a device, and control a client server to approve, hold, or deny the online application based on the fraud risk score that is determined.

Abstract: User identification with blended response from dual-layer identification service. In one embodiment, a server comprising an electronic processor configured to detect an access request by a user of a user interface device, retrieve a plurality of input profile records from an input profile record repository, perform an identification of the user with one or more passive biometrics models and the plurality of input profile records that are retrieved, generate an identification response and an additional identification request based on an outcome of the identification of the user, control the communication interface to transmit the additional identification request to the second server via the network, receive a second identification response from the second server, and generate a blended response by modifying one or more characteristics of the identification response with the second identification response, the blended response indicating the identification of the user.

Abstract: Methods and systems for rule-based machine learning for precise fraud detection. One system includes an electronic processor configured to determine, via a decision tree, a first subset of datasets of an aggregate dataset collection generated using a rule-based model. The electronic processor is also configured to select a third collection of datasets, each dataset included in the third collection of datasets associated with a user characteristic associated with fraud. The electronic processor is also configured to determine, via the decision tree, a second subset of datasets of the third collection of datasets, each dataset included in the second subset of datasets associated with a second set of user characteristics associated with fraud. The electronic processor is also configured to, in response to determining that an accuracy score associated with the second set of user characteristics satisfies a threshold, generate and transmit a report for display.

Abstract: Enterprise cybersecurity systems and methods related to receiving or accessing a real-time cybersecurity request, automatically transmitting a live mode request to a first cybersecurity microservice to generate cybersecurity risk-scoring information, receiving a response to the live mode request, calculating a cybersecurity score, automatically populating cybersecurity response data based on the calculated cybersecurity score, and automatically transmitting the cybersecurity response data to the enterprise client electronic device. The enterprise cybersecurity system and method also includes, after the cybersecurity response data has been transmitted to the enterprise client electronic device, automatically transmitting a test mode request to at least one test mode cybersecurity microservice, wherein the test mode request includes real-time data extracted from the real-time cybersecurity request.

Abstract: Methods and systems for providing visual dashboards for cloud-based products. One system includes an electronic processor configured to receive a request for a visual dashboard. The electronic processor is also configured to execute a first application programming interface (API) call to a first API-based product to access a first configuration file associated with the first API-based product. The electronic processor is also configured to receive a first API response from the first API-based product. The electronic processor is also configured to generate and output, based on the first configuration file, the visual dashboard, the visual dashboard providing information associated with the first API-based product.

Abstract: Embodiment described herein provide a system for authenticating an identity of a user. The system includes a first device, a second device, a user device, and a remote computing device. The first device, the second device, and the user device are connected to and operable to communicate over the first communications network. The user device is connected to and operable to communicate over a second communications network. The user device is configured to detect the first device or the second device when the first device or the second device is connected the first communications network, and to generate one or more signals related to the detection of the first device or the second device. The remote computing device is configured to receive the one or more signals from the user device and authenticate the identity of the user based on the one or more signals.

Abstract: Systems and methods for tracking velocity information. One system includes an application execution server providing an application layer. The application execution server is configured to receive a request including metadata. The application execution server is also configured to generate and transmit a response to the request. The application execution server is also configured to enrich the metadata by structuring the metadata for further processing by a data processing layer, where the further processing includes determining velocity information associated with the metadata, and by supplementing the metadata with available historical velocity information. The application execution server is also configured to transmit the enriched metadata for further processing by the data processing layer.

Abstract: Embodiments described herein provide for structured command line input for application integration such that application programming interface (API) integration is simplified for end users. The embodiments described herein use OpenAPI specifications to map specification paths (or endpoints) to a command line interface (CLI) command structure automatically. Accordingly, the embodiments described herein enable an end user to run CLI commands within the end user's environment to accomplish application integration without the need for a technician to write configuration instructions for each type of user device. Additionally, the embodiments described herein enable efficient product integration at a larger scale as well as the ability to ad hoc add new solutions or applications based on, for example, a single line of CLI input.

Abstract: Methods and systems for providing configuration management using customized request headers. One system includes an electronic processor configured to receive, from a requesting device, a request including a customized request header, the customized request header including a custom configuration parameter. The electronic processor is also configured to perform a fraud analysis function based on the request. The electronic processor is also configured to generate a response including a customized response body based on the customized request header. The electronic processor is also configured to transmit the response to the requesting device, where the requesting device performs functionality based on information included in the customized response body.

Abstract: A system for generating a fraud risk score for a third party provider (TPP) transaction. The system includes a server including an electronic processor. The electronic processor is configured to determine a frequency-recency-monetary value feature, a reputation feature, and a rule feature for a TPP transaction, using the frequency-recency-monetary value feature, the reputation feature, and the rule feature as input for a machine learning model, execute the machine learning model to generate a blended score, and, when the blended score is above a second predetermined threshold, determine that the TPP transaction is fraudulent.

Abstract: A fraud prevention server and method. The fraud prevention server includes an electronic processor and a memory. The memory includes an online application origination (OAO) service. The electronic processor is configured to perform feature encoding on one or more categorical variables in a first matrix to generate one or more feature encoded categorical variables, generate a second matrix including the one or more feature encoded categorical variables, generate a feature encoded OAO model by training an OAO model with the second matrix, receive information regarding a submission of an online application on a device, determine a fraud score of the online application based on the information that is received and the feature encoded OAO model, the feature encoded OAO model being more precise than the OAO model, and control the client server to approve, hold, or deny the online application based on the fraud score that is determined.

Abstract: Devices, systems, and methods with behavioral one-time-passcode (OTP) generation. In one example, a server includes a memory and an electronic processor communicatively connected to the memory. The memory includes a behavioral one-time-passcode (OTP) program and a user profile repository. The electronic processor, when executing the behavioral OTP program, is configured to receive a one-time-passcode (OTP) request, generate a behavioral one-time-passcode (OTP) based on a user profile stored in the user profile repository in response to receiving the OTP request, and output the behavioral OTP that is generated.

Abstract: User identification with blended response from dual-layer identification service. In one embodiment, a server comprising an electronic processor configured to detect an access request by a user of a user interface device, retrieve a plurality of input profile records from an input profile record repository, perform an identification of the user with one or more passive biometrics models and the plurality of input profile records that are retrieved, generate an identification response and an additional identification request based on an outcome of the identification of the user, control the communication interface to transmit the additional identification request to the second server via the network, receive a second identification response from the second server, and generate a blended response by modifying one or more characteristics of the identification response with the second identification response, the blended response indicating the identification of the user.

Abstract: A fraud prevention server that includes an electronic processor and a memory. The memory includes an online application origination (OAO) service and a plurality of OAO models, each of the plurality of OAO models differentiates between a behavior of a normal user and a behavior of a nefarious actor during a submission of the online application on a device. When executing the OAO service, the electronic processor is configured to receive form data from a client server, determine a best OAO model from a plurality of OAO models with deep-learning, determine a fraud score of the online application based on the best OAO model, and control the client server to approve, hold, or deny the online application based on the fraud score that is determined.

Abstract: A system for utilizing behavioral features to authenticate a user entering login credentials. The system includes an electronic processor configured to receive a request to access a user account and compare behavioral features included in the request to behavioral features included in a user behavior profile associated with the user account. The electronic processor is also configured to, based on the comparison, generate one or more scores. The electronic processor is further configured to, for each of the one or more scores, compare the score to a predetermined threshold and, based on the comparison of the score to the predetermined threshold, adjust a match value. The electronic processor is also configured to compare the match value to one or more predetermined thresholds to determine whether the behavioral features included in the request to access the user account authenticates the user, does not authenticate the user, or is inconclusive.

Abstract: Systems and methods are provided for extending dialogue between a mobile device and a point-of-interaction (POI). One example computer-implemented method includes receiving, by a conduit server, from the POI, via a first connection between the conduit server and the POI, a request for an extended connection between the POI and the mobile device, and notifying a host server of the request. The method then includes receiving, from the mobile device, via a second connection between the conduit server and the mobile device, an identifier for the mobile device and pairing, based on the identifier, the first and second connections, to establish a wireless connection between the POI and the mobile device, via the conduit server. The method further includes submitting a wake-up call to the mobile device, whereby the mobile device publishes the identifier to establish a wireless connection directly between the POI and the mobile device.

Abstract: An anomaly detection system that includes a database and a server. The server is connected to the database. The server is configured to identify anomalous web traffic for a certain time period based on one or more client keys from the certain time period. The client key(s) includes at least two characteristics related to web traffic data. The server includes a processing unit and a memory. The server is configured to receive the web traffic data from the database, calculate a z-score metric for the client key, calculate a change rate metric for the client key, calculate a failure metric for the client key, determine an anomaly score based on the z-score metric, the change rate metric, and the failure metric, and determine that the certain time period is an anomalous time period based on the anomaly score.

Abstract: A system for determining a fraud risk score associated with a transaction. The system includes a server including an electronic processor. The electronic processor is configured to determine a plurality of rules based on a plurality of transactions over time and extract one or more features of the transaction. The electronic processor is also configured to select, based on the plurality of rules, a plurality of fraud risk features Each non-categorical fraud risk feature selected is associated with a fraud risk feature value and each categorical fraud risk feature selected is associated with a categorical variable value. The electronic processor is configured to determine, for each categorical fraud risk feature, a fraud risk feature value. The electronic processor is also configured to determine the fraud risk score based on the one or more of the transformed fraud risk feature values.

Abstract: Systems, methods, devices, and computer readable media related to fraud detection. Fraud detection is achieved using a flexible scripting language and syntax that simplifies the generation of fraud detection rules. The rules are structured as conditional IF-THEN statements that include data objects referred to as Anchors and Add-Ons. The Anchors and Add-Ons used to generate the rules also correspond to a distinct data path for the retrieval data from any of a variety of data sources. The retrieval of data from the various data sources is optimized based on data dependencies within the rules. By knowing the data dependencies of each rule and utilizing parallelization of rule execution, the retrievals of data from the data sources is achieved efficiently so the rules can be executed quickly.