Invention Grant
- Patent Title: Forecasting and classifying cyber-attacks using analytical data based neural embeddings
-
Application No.: US15914483Application Date: 2018-03-07
-
Publication No.: US10554680B2Publication Date: 2020-02-04
- 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; James Nock
- Main IPC: H04L29/06
- IPC: H04L29/06

Abstract:
A first collection including an analytical 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 at least one of vectors of the second collection with a corresponding vector of a fourth collection, migrating at least one of the vectors of the second collection with a corresponding vector of a fifth collection. Using a forecasting configuration, an analytical feature vector of the third collection is aged to generate a changed analytical feature vector containing analytical feature values expected at a future time. The changed analytical 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
- US20180198816A1 FORECASTING AND CLASSIFYING CYBER-ATTACKS USING ANALYTICAL DATA BASED NEURAL EMBEDDINGS Public/Granted day:2018-07-12
Information query