Bank of America Corporation

Christopher Lee Danielson

Daniel Joseph Serna

Patrick Nicholas Lawrence

Marcus Raphael Matos

Robert Justin Brown

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.

Threat detection and prevention for information systems

A system is configured for associating a CVE with a particular device profile is disclosed. The system receives a request from a user to associate a CVE with a particular device profile. For each device profile from a plurality of device profiles stored in a memory, the system determines feature importance values for features of each device profile. The features of each device profile include at least an operating system and a CPU architecture. The feature importance value of a corresponding feature of a device profile associated with a CVE indicates a probability of the CVE to affect the device profile with respect to that feature. The system identifies a device profile that has features with a total feature importance value above a feature importance threshold value. The system identifies a particular CVE associated with the identified device profile. The system associates the particular CVE with the particular device profile.

System and method for associating a common vulnerability and exposures (CVE) with a computing device and applying a security patch

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.

Detecting bad actors within information systems

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.

Threat detection within information systems

Systems, computer program products, and methods are described herein for software compiler integrity verification. The present invention is configured to retrieve, from a source code repository, a source code; process, using a first build machine, the source code into a first object code; process, using a second build machine, the source code into a second object code; initiate an integrity verification engine on the first object code and the second object code; decompile, using the integrity verification engine, the first object code to create a first decompiled object code and the second object code to create a second decompiled object code; compare the first decompiled object code with the second decompiled object code; determine a match between the first decompiled object code and the second decompiled object code; and transmit an approval notification.

System for software compiler integrity verification

A system for classifying a data item to communicate to authorized users extracts features from the data item, where the features comprise a responsibility feature and a sensitivity feature. The responsibility feature indicates a job responsibility associated with the data item. The sensitivity feature indicates a sensitivity level of the data item. The system determines, based on the responsibility feature, that the data item belongs to a particular responsibility class. The system determines, based on the sensitivity feature, that the data item belongs to a particular sensitivity class. The system determines whether a user to whom the data item is directed belongs to the particular responsibility class and sensitivity class to which the data item belongs. The system sends the data item to the user, if is it determined that the user belongs to the particular responsibility class and sensitivity class to which the data item belongs.

System and method for routing data to authorized users based on security classification of data

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.

Optimizing software codebases using advanced code complexity metrics

A decoding device that includes a decoding engine implemented by a processor connected to a memory. The decoding engine is configured to receive an encoded signal. The encoding engine is further configured to determine an encoded signal byte value at an encoded signal byte location in the encoded signal. The encoded signal byte location is mapped to a key map byte location in a key map. The decoding engine is further configured to determine a key map byte value at the key map byte location in the key map. The decoding engine is further configured to set a decoded signal byte value with the encoded signal byte value at the decoded signal byte location in a decoded signal. The decoding engine is further configured to output the decoded signal.

Network communication decoder using key pattern encryption

An encoding device that includes an encoding engine implemented by a processor connected to a memory. The memory stores a key map that encodes a data signal with a noise signal. The encoding engine is configured to obtain a data signal and generate a noise signal. The encoding engine is further configured to determine a key map byte value at a key map byte location in the key map. The key map byte value indicates a data signal byte location in the data signal. The encoding engine is further configured to determine a data signal byte value at the data signal byte location in the data signal and overwrite a noise signal byte value with the data signal byte value at a noise signal byte location in the noise signal. The encoding engine is configured to transmit the encoded signal.

Growing community of inventors

Poway, CA, United States of America

Christopher Lee Danielson