Abstract: A method of automatically manipulating a lifecycle of a honeyfile on a file system, includes: implementing a learning algorithm on a file system; identifying a real file; training the learning algorithm by observing temporal events involving the real file; training a model with the learning algorithm; implementing a user agent on the file system; creating a honeyfile on the file system; deploying the honeyfile on the file system; using the trained model to generate user agent actions; evaluating the honeyfile; and either: deleting the honeyfile; or re-using the trained model to generate user agent actions to automatically modify the honeyfile.

Abstract: This disclosure relates to a method for generating simulated communication data. A processor trains a model on historical communication data. The model comprises a probability distribution to model time, sender, and recipients and a parameter vector that jointly encodes information from the input event sequence of time value, the sender and the recipients. The probability distribution is based on the parameter vector. The processor generates a collection of simulated communication messages by sampling from the probability distribution to determine a time value, a sender, and recipients of the simulated communication message, wherein the recipients are dependent on the sender of the simulated message. The processor generates a simulated natural language text for the message, which is part of a simulated conversation between the sender and the recipients.

Abstract: The present disclosure is directed to systems and methods for creating, managing, and storing synthetic data that is designed to be placed within databases having real data to deceive, attract, detect and/or track unauthorized access thereof. The synthetic data can be stored as a honeyrecord in a database table that is interspersed with real data such that the honeyrecord is indistinguishable from real data or combined to form a honeytable having multiple honeyrecords therein. In some embodiments, the synthetic data can be used to create a user persona having one or more parameters to construct a profile or an identity honeyrecord of an individual. The parameters can include private information such as name, sex, passport number, social security number, bank account or credit card details, biometric details, passwords, and other information that is attractive to hackers and other data thieves.

Abstract: Provided herein, in certain aspects, are methods for the treatment of a CD20-expressing cancer in a human subject, comprising administration of a bispecific anti-CD20×anti-CD3 antibody (e.g., XmAb® 13676) to the human subject in a flat escalation dosing regimen including at least four phases (e.g., five phases or more than five phases).

Abstract: A method for automatically creating a honeyfile for a file system, includes the steps of: surveying a file set of the file system to identify tokenisable data in the file set, tokenising the identified tokenisable data to form a plurality of token sequences, and either selecting one of the plurality of token sequences or generating a token sequence to operate as an exemplar token sequence; applying a substitution method to substitute the tokens of the exemplar token sequence with replacement tokenisable data; and packaging the replacement tokenisable data into a honeyfile.

Abstract: A method for automatically creating a honeyfile for a file system, includes the steps of: surveying a file set of the file system to identify tokenisable data in the file set, tokenising the identified tokenisable data to form a plurality of token sequences, and either selecting one of the plurality of token sequences or generating a token sequence to operate as an exemplar token sequence; applying a substitution method to substitute the tokens of the exemplar token sequence with replacement tokenisable data; and packaging the replacement tokenisable data into a honeyfile.

Abstract: Provided herein, in certain aspects, are methods for the treatment of a CD20-expressing cancer in a human subject, comprising administration of a bispecific anti-CD20×anti-CD3 antibody.

Abstract: A method of automatically manipulating a lifecycle of a honeyfile on a file system, includes: implementing a learning algorithm on a file system; identifying a real file; training the learning algorithm by observing temporal events involving the real file; training a model with the learning algorithm; implementing a user agent on the file system; creating a honeyfile on the file system; deploying the honeyfile on the file system; using the trained model to generate user agent actions; evaluating the honeyfile; and either: deleting the honeyfile; or re-using the trained model to generate user agent actions to automatically modify the honeyfile.

Abstract: The present disclosure is directed to systems and methods for creating, managing, and storing synthetic data that is designed to be placed within databases having real data to deceive, attract, detect and/or track unauthorized access thereof. The synthetic data can be stored as a honeyrecord in a database table that is interspersed with real data such that the honeyrecord is indistinguishable from real data or combined to form a honeytable having multiple honeyrecords therein. In some embodiments, the synthetic data can be used to create a user persona having one or more parameters to construct a profile or an identity honeyrecord of an individual. The parameters can include private information such as name, sex, passport number, social security number, bank account or credit card details, biometric details, passwords, and other information that is attractive to hackers and other data thieves.

Abstract: A method for automatically creating a honeyfile for a file system, includes the steps of: surveying a file set of the file system to identify tokenisable data in the file set, tokenising the identified tokenisable data to form a plurality of token sequences, and either selecting one of the plurality of token sequences or generating a token sequence to operate as an exemplar token sequence; applying a substitution method to substitute the tokens of the exemplar token sequence with replacement tokenisable data; and packaging the replacement tokenisable data into a honeyfile.

Abstract: A method for automatically creating a honeyfile for a file system, includes the steps of: surveying a file set of the file system to identify tokenisable data in the file set, tokenising the identified tokenisable data to form a plurality of token sequences, and either selecting one of the plurality of token sequences or generating a token sequence to operate as an exemplar token sequence; applying a substitution method to substitute the tokens of the exemplar token sequence with replacement tokenisable data; and packaging the replacement tokenisable data into a honeyfile.

Abstract: A method for automatically creating a honeyfile for a file system, includes the steps of: surveying a file set of the file system to identify tokenisable data in the file set, tokenising the identified tokenisable data to form a plurality of token sequences, and either selecting one of the plurality of token sequences or generating a token sequence to operate as an exemplar token sequence; applying a substitution method to substitute the tokens of the exemplar token sequence with replacement tokenisable data; and packaging the replacement tokenisable data into a honeyfile.

Abstract: Disclosed are methods for the treatment of diabetes by administering a composition comprising a pharmaceutically acceptable amount of a dual inhibitor of both 5-lipoxygenase (5-LO) and 12/15-lipoxygenase (12/15-LO) enzymes or of both 5-lipoxygenase (5-LO) and 15-lipoxygenase (15-LO) enzymes. The invention is also directed to methods of controlling blood glucose level in diabetic patients with a dual inhibitor of both 5-lipoxygenase (5-LO) and 12/15-lipoxygenase (12/15-LO) enzymes or of both 5-lipoxygenase (5-LO) and 15-lipoxygenase (15-LO) enzymes.