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Небесная энциклопедия

Космические корабли и станции, автоматические КА и методы их проектирования, бортовые комплексы управления, системы и средства жизнеобеспечения, особенности технологии производства ракетно-космических систем

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Мониторинг СМИ

Мониторинг СМИ и социальных сетей. Сканирование интернета, новостных сайтов, специализированных контентных площадок на базе мессенджеров. Гибкие настройки фильтров и первоначальных источников.

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Применить Всего найдено 2. Отображено 2.
11-03-2021 дата публикации

MACHINE LEARNING TECHNIQUES FOR INTERNET PROTOCOL ADDRESS TO DOMAIN NAME RESOLUTION SYSTEMS

Номер: US20210073661A1
Автор: Halecky Nicholaus Eugene, Lin Benny, Matlick Erik G.
Принадлежит: Bombora, Inc.

An IP-to-Domain (IP2D) resolution system predicts which domain is most likely associated with an IP address. The resolution system generates unique source vote features (FSV) from (IP, domain, source) data. The FSV features are used to train a computer learning model that predicts which domain is most likely associated with an IP address. The domain predictions can then be used to more efficiently process events, more accurately calculate consumption scores, and more accurately detect associated company surges. 1. One or more non-transitory computer readable media (NTCRM) comprising instructions for predicting Internet Protocol (IP) to Domain (IP2D) mappings using machine learning techniques , wherein execution of the instructions by one or more processors is operable to cause a computing system to:identify a set of source vote features from raw IP2D source data;generate scaled IP2D features based on feature scaling and dimensional reduction operations performed on the set of source vote features;apply the scaled IP2D features to an IP-domain classification model to obtain a prediction dataset, the prediction dataset indicating a probability that at least one domain maps to at least one IP address.2. The one or more NTCRM of claim 1 , wherein execution of the instructions is further operable to cause the computing system to:generate a training dataset;perform binary classification on the training dataset; andgenerate the IP-domain classification model based on the binary classification.3. The one or more NTCRM of claim 2 , wherein execution of the instructions is further operable to cause the computing system to:perform feature scaling and dimensional reduction on the training dataset; andperform binary classification on the feature scaled and dimension reduced training dataset.4. The one or more NTCRM of claim 3 , wherein claim 3 , to generate the training dataset claim 3 , execution of the instructions is operable to cause the computing system to:generate labeled ...

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Номер записи: 1
26-09-2019 дата публикации

WEBSITE FINGERPRINTING

Номер: US20190294642A1
Автор: Armstrong Robert J., Halecky Nicholaus Eugene, Lin Benny, Matlick Erik G.
Принадлежит:

A website classification system identifies one or more features in websites and uses the features to classify the websites. The website classification system may generate features identifying structural semantics of webpages, content semantics of webpages, content interaction behavior with the webpages, or types of users accessing the webpages. The website classification system may generate vectors that represent the different features. A first set of vectors from classified websites are used for training a computer learning model. Vectors from unclassified websites are then fed into the trained learning model to predict a particular website classification. The predicted website classifications provide more accurate intent, consumption, and surge score predictions. 1. A computer program stored on a non-transitory storage medium , the computer program comprising a set of instructions , when executed by a hardware processor , cause the hardware processor to:identify one or more features from training websites with known classifications;train a computer learning model with the features and known classifications;identify the features from an unclassified website with an unknown classification; andapply the features from an unclassified website to the trained computer learning model to predict a classification for the unclassified website.2. The computer program of claim 1 , wherein the set of instructions claim 1 , when executed by a hardware processor claim 1 , further cause the hardware processor to:generate a first set of vectors representing the features of the training websites;use the first set of vectors and known classifications of the training websites to train the computer learning model;generate a second set of vectors representing the features of the unclassified website; andapply the second set of vectors to the trained computer learning model to classify the unclassified website.3. The computer program of claim 1 , wherein one of the features identifies ...

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Номер записи: 2