Abstract: A device that is configured to receive user activity information that includes information about user interactions with a network device for a plurality of users. The device is further configured to input the user activity information into a first machine learning model that is configured to receive user activity information and to output a set of bad actor candidates based on the user activity information. The device is further configured to filter the user activity information based on the set of bad actor candidates. The device is further configured to input the filtered user activity information into a second machine learning model that is configured to receive the filtered user activity information and to output system exposure information that identifies network security threats. The device is further configured to identify network security actions based on the network security threats and to execute the network security actions.

Abstract: A device that is configured to receive user activity information that includes information about user interactions with a network device for a plurality of users. The device is further configured to input the user activity information into a first machine learning model that is configured to receive user activity information and to output a set of bad actor candidates based on the user activity information. The device is further configured to filter the user activity information based on the set of bad actor candidates. The device is further configured to input the filtered user activity information into a second machine learning model that is configured to receive the filtered user activity information and to output system exposure information that identifies network security threats. The device is further configured to identify network security actions based on the network security threats and to execute the network security actions.

Abstract: A device that is configured to receive user activity information that includes information about user interactions with a network device for a plurality of users. The device is further configured to receive a set of bad actor candidates that identifies one or more users from among the plurality of users. The device is further configured to filter the user activity information based on the set of bad actor candidates. The device is further configured to input the filtered user activity information into a machine learning model. The machine learning model is configured to receive the filtered user activity information and to output system exposure information that identifies network security threats. The device is further configured to identify network security actions based on the network security threats and to execute the network security actions.

Abstract: Methods and systems for using block chain technology to verify transaction data are described herein. A computing platform may receive data about events related to transactions, personal or corporate information, supply chains, and other relevant information about a person or corporate entity. The event information may be received, aggregated, and processed to determine metadata about the person or corporate entity. The metadata may indicate, for example, a trustworthiness of the person or corporate entity for various purposes. Such event information and/or metadata may be stored as transactions in a block chain that may be accessible by counterparties to a potential transaction involving the person or corporate entity. The automated event processing computing platform may further use automated techniques to implement smart transactions between the person/entity and counterparty based on the trust metadata.

Abstract: A system that includes a first edge node and a second edge node in signal communication with one or more interior nodes. The first edge node is configured to receive a correlithm object from a first device outside of the network, to identify an input correlithm object from the first node table with the shortest distance, to fetch a corresponding correlithm object from the first node table, and to send the correlithm object to the one or more interior nodes. The second edge node is configured to receive a correlithm object from the one or more interior nodes, to identify an input correlithm object from a second node table with the shortest distance, to fetch a corresponding correlithm object from the second node table, and to send the correlithm object to a second device outside of the network.

Abstract: A system includes a memory and a node. The memory stores first and second log string correlithm objects. The node receives first and second real-world numerical values, and identifies a first sub-string correlithm object from the first log string correlithm object that corresponds to the first real-world numerical value. The node aligns the first and second log string correlithm objects such that the first sub-string correlithm object aligns with a sub-string correlithm object from the second log string correlithm object representing the logarithmic value of one. The node identifies a second sub-string correlithm object from the second log string correlithm object that corresponds to the second real-world numerical value, and determines which sub-string correlithm object from the first log string correlithm object aligns with the second sub-string correlithm object from the second log string correlithm object. The node outputs the determined sub-string correlithm object.

Abstract: Methods and systems for using block chain technology to verify transaction data are described herein. A computing platform may receive data about events related to transactions, personal or corporate information, supply chains, and other relevant information about a person or corporate entity. The event information may be received, aggregated, and processed to determine metadata about the person or corporate entity. The metadata may indicate, for example, a trustworthiness of the person or corporate entity for various purposes. Such event information and/or metadata may be stored as transactions in a block chain that may be accessible by counterparties to a potential transaction involving the person or corporate entity. The automated event processing computing platform may further use automated techniques to implement smart transactions between the person/entity and counterparty based on the trust metadata.

Abstract: A device that is configured to receive user activity information that includes information about user interactions with a network device for a plurality of users. The device is further configured to receive a set of bad actor candidates that identifies one or more users from among the plurality of users. The device is further configured to filter the user activity information based on the set of bad actor candidates. The device is further configured to input the filtered user activity information into a machine learning model. The machine learning model is configured to receive the filtered user activity information and to output system exposure information that identifies network security threats. The device is further configured to identify network security actions based on the network security threats and to execute the network security actions.

Abstract: A device that is configured to receive user activity information that includes information about user interactions with a network device for a plurality of users. The device is further configured to input the user activity information into a first machine learning model that is configured to receive user activity information and to output a set of bad actor candidates based on the user activity information. The device is further configured to filter the user activity information based on the set of bad actor candidates. The device is further configured to input the filtered user activity information into a second machine learning model that is configured to receive the filtered user activity information and to output system exposure information that identifies network security threats. The device is further configured to identify network security actions based on the network security threats and to execute the network security actions.

Abstract: A device that is configured to receive user activity information from a network device. The user activity information includes information about user interactions with the network device for a plurality of users. The device is further configured to input the user activity information into a machine learning model. The machine learning model is configured to receive user activity information and to output a set of bad actor candidates based on the user activity information. The set of bad actor candidates identifies one or more users from among the plurality of users. The device is further configured to receive the set of bad actor candidates from the machine learning model and to output the set of bad actor candidates.

Abstract: A system includes a memory and a node. The memory stores first and second log string correlithm objects. The node receives first and second real-world numerical values, and identifies a first sub-string correlithm object from the first log string correlithm object representing the first real-world numerical value and a second sub-string correlithm object from the second log string correlithm object representing the second real-world numerical value. The node aligns the first and second log string correlithm objects such that the first sub-string correlithm object aligns with the second sub-string correlithm object. The node identifies a sub-string correlithm object from the second log string correlithm object representing the logarithmic value of one. The node determines which sub-string correlithm object from the first log string correlithm object aligns with the identified sub-string correlithm object from the second log string correlithm object. The node outputs the determined sub-string correlithm object.

Abstract: A device receives a document that includes a request and supporting data associated with the request. The device determines that the received request is a first request type. A first set of supporting data is determined that is needed to determine a verification status for requests of the first request type. Based on a comparison of the received supporting data to the first set of supporting data, the device determines that the supporting data includes the data needed to determine the verification status for requests of the first request type. The device then extracts document data from the received document. The extracted document data is compared to the supporting data. Based on this comparison, a verification status is determined for the request.

Abstract: A device that includes a model training engine implemented by a processor. The model training engine is configured to select a first sub-string correlithm object and a second sub-string correlithm object from a set of sub-string correlithm objects. The model training engine is further configured to compute a Hamming distance between the first sub-string correlithm object and the second sub-string correlithm object and to compare the Hamming distance to a bit difference threshold value. The model training engine is further configured to determine that the Hamming distance is less than the bit difference threshold value and to compute an average of the first sub-string correlithm object and the second sub-string correlithm object in the n-dimensional space in response to the determination. The model training engine is further configured to train the machine learning model to define the average as a centroid for the first cluster.

Abstract: A device comprising a cluster engine implemented by a processor. The cluster engine is configured to obtain a reference correlithm object and compute a set of Anti-Hamming distances between the reference correlithm object and the set of correlithm objects. The cluster engine is further configured to identify a subset of correlithm objects from the set of correlithm objects that are associated with an Anti-Hamming distance that is greater than a first bit threshold value. The cluster engine is further configured to compute a set of Hamming distances between the reference correlithm object and the subset of correlithm objects and to identify correlithm objects associated with a Hamming distance that exceeds a second bit threshold value. The cluster engine is further configured to remove the identified correlithm objects that are associated with a Hamming distance that exceeds the second bit threshold value and generate the cluster.

Abstract: A device that includes a model training engine implemented by a processor. The model training engine is configured to obtain a set of data values associated with a feature vector. The model training engine is further configured to generate a set of gradients by dividing separation distances by an average separation distance and to compare each gradient to a gradient threshold value. The model training engine is further configured to identify a boundary in response to determining a gradient exceeds the gradient threshold value, to determine a number of identified boundaries, and to determine a number of clusters based on the number of identified boundaries. The model training engine is further configured to train the machine learning model to associate the determined number of clusters with the feature vector.

Abstract: A device that includes a converter engine configured to receive an input signal at one of a first input and a second input. In response to receiving the input signal at the first input, the device is configured to identify a real world value in a converter table based on the input signal, fetch a correlithm object linked with the real world value, and to output the identified correlithm object as the first output signal. In response to receiving the input signal at the second input, the device is configured to identify a correlithm object from the converter table with the shortest distance, to fetch a real world value from the converter table linked with the identified correlithm object, and to output the identified real world value as the second output signal.

Abstract: Aspects of the disclosure relate to codebase effort tracking. A computing platform may detect accessing of a first code resource by a user of a computing device and initiate tracking of a first interaction time associated with the first code resource. Subsequently, the computing platform may detect loss of interaction with the first code resource and terminate tracking of the first interaction time. Then, the computing platform may detect accessing of a second code resource by the user of the computing device and initiate tracking of a second interaction time associated with the second code resource. Based on the tracking of the respective interaction times, the computing platform may generate and store a code complexity metric. Then, the computing platform may repeat one or more steps for a third code resource and update the code complexity metric based on a third interaction time associated with the third code resource.

Abstract: A device receives a document that includes a request and supporting data associated with the request. The device determines that the received request is a first request type. A first set of supporting data is determined that is needed to determine a verification status for requests of the first request type. Based on a comparison of the received supporting data to the first set of supporting data, the device determines that the supporting data includes the data needed to determine the verification status for requests of the first request type. The device then extracts document data from the received document. The extracted document data is compared to the supporting data. Based on this comparison, a verification status is determined for the request.

Abstract: A device that includes a model training engine implemented by a processor. The model training engine is configured to obtain a set of data values associated with a feature vector. The model training engine is further configured to transform a first data value and a second data value from the set of data value into sub-string correlithm objects. The model training engine is further configured to compute a Hamming distance between the first sub-string correlithm object and the second sub-string correlithm object and to identify a boundary in response to determining that the Hamming distance exceeds a bit difference threshold value. The model training engine is further configured to determine a number of identified boundaries, to determine a number of clusters based on the number of identified boundaries, and to train the machine learning model to associate the determined number of clusters with the feature vector.

Abstract: A correlithm object processing system that includes a trainer configured to receive a real world input value and a real world output value. The trainer is further configured to send the real world input value to a sensor engine and to receive a source correlithm object in response to sending the real world value to the sensor engine. A source correlithm object is a point in an n-dimensional space represented by a binary string. The trainer is further configured to send a real world output value to an actor engine and to receive a target correlithm object in response to sending the real world output value to the actor engine. A target correlithm object is a point in the n-dimensional space represented by a binary string. The trainer is further configured to generate an entry in a node table linking the source correlithm object with the target correlithm object.