Invention Grant
- Patent Title: Forecasting and classifying cyber-attacks using crossover neural embeddings
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Application No.: US15815393Application Date: 2017-11-16
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Publication No.: US10015190B2Publication Date: 2018-07-03
- Inventor: Mohamed N. Ahmed , Aaron K. Baughman , John F. Behnken , Mauro Marzorati
- Applicant: International Business Machines Corporation
- Applicant Address: US NY Armonk
- Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
- Current Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
- Current Assignee Address: US NY Armonk
- Agency: Garg Law Firm, PLLC
- Agent Rakesh Garg; Christopher K. McLane
- Main IPC: H04L29/06
- IPC: H04L29/06 ; G06N3/08 ; G06N7/00

Abstract:
A first collection including a first feature vector and a Q&A feature vector is constructed. A second collection is constructed from the first collection by inserting noise in at least one of the vectors. A third collection is constructed by crossing over a feature vectors of the second collection with a corresponding feature vector of a fourth collection. The second and the fourth collections have a property similar to one another. Using a forecasting configuration, a vector of the third collection is aged to generate a changed feature vector, the changed feature vector containing feature values expected at a future time. The changed feature vector is input into a trained neural network to predict a probability of the cyber-attack occurring at the future time.
Public/Granted literature
- US20180084004A1 FORECASTING AND CLASSIFYING CYBER-ATTACKS USING CROSSOVER NEURAL EMBEDDINGS Public/Granted day:2018-03-22
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