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US Patent 11632386 Cyberattack forecasting using predictive information

Patent 11632386 was granted and assigned to Rochester Institute of Technology on April, 2023 by the United States Patent and Trademark Office.

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Is a
Patent
Patent
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Patent attributes

Current Assignee
Rochester Institute of Technology
Rochester Institute of Technology
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
116323860
Patent Inventor Names
Shanchieh Jay Yang0
Ahmet Okutan0
Katie McConky0
Date of Patent
April 18, 2023
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Patent Application Number
168986180
Date Filed
June 11, 2020
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Patent Citations
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US Patent 10554680 Forecasting and classifying cyber-attacks using analytical data based neural embeddings
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US Patent 10601845 System and method for predictive attack sequence detection
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US Patent 10248910 Detection mitigation and remediation of cyberattacks employing an advanced cyber-decision platform
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US Patent 10404748 Cyber risk analysis and remediation using network monitored sensors and methods of use
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US Patent 10594714 User and entity behavioral analysis using an advanced cyber decision platform
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Patent Primary Examiner
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Sarah Su
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CPC Code
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G06N 5/04
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H04L 63/164
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H04L 63/1433
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H04L 63/0272
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G06N 20/00
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A computerized method and system for predicting the probability of a cyberattack to a target entity, includes: collecting a plurality of predictive signals to a target entity for a specific cyberattack type; optionally, imputing a value for missing values of the collected signals; selecting a set of relevant non-redundant signals from the collected signals to create lagged signals; identifying from the lagged signals relevant data chunks to form a custom training set of signals; providing selected ground truth data related to the specific attack type for the target entity; training a forecasting model using the custom training set of signals together with the selected ground truth data related to the specific attack type for the target entity to generate a trained forecasting model; providing a second set of signals of the same type of signals as the custom training set of signals; and generating the probability of the specific attack type of interest against the target entity by inputting the second set of signals into the trained forecasting model.

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