Метод оценки степени связанности профилей пользователей социальной сети на основе открытых данныхстатья из журнала
База данных: Каталог библиотеки СФУ (К 290)
Библиографическое описание: Катаева, Валентина Алексеевна. Метод оценки степени связанности профилей пользователей социальной сети на основе открытых данных = Estimation method of the cohesion degree for the users’ profiles of social network based on open data / В. А. Катаева, И. С. Пантюхин, И. В. Юрин. - (Методическое обеспечение). - Текст : непосредственный // Открытое образование. - 2017. - № 6. - С. 14-22. - Библиогр.: с. 20-22 (20 назв.). - ISSN 1818-4243.
Аннотация: Работа метода демонстрируется на примере социальной сети "Вконтакте". Данный метод включает в себя последовательность следующих этапов: на первом этапе происходит сбор данных о пользователях социальной сети с помощью API и формирование кортежей признаков профилей пользователей. Кортеж признаков профилей социальной сети — это собранные для каждого из пользователей данные, хранящиеся в структурированном виде. Следующий этап — анализ собранной информации. Для каждого признака из кортежа профилей, т. е. возможного элемента взаимодействия пользователей в социальной сети, рассчитывается коэффициент связанности по признаку. Также для каждого признака рассчитывается его информативность, т. е. на сколько важен тот или иной признак в данной социальной сети. На заключительной этапе происходит формирование результатов с помощью выведенной в процессе исследования формулы вероятности связи двух пользователей. Полученная в результате применения метода вероятность связи двух пользователей может применяться для оптимизации деятельности оперативно-розыскных служб и специальных органов. Также полученная степень связанности двух пользователей может интерпретироваться как вероятность возникновения канала утечки информации между ними.
The purpose of research was to study the existing methods of determining the degree of cohesion of two users of social network, identifying their shortcomings and developing a new method. The research identified shortcomings of existing methods and proposed a new method for assessing the degree of cohesion of social network profiles based on open data from a social network. Under the degree of cohesion of users’ profiles is understood the probability of communication (interaction) of profile owners in real life, it is calculated for two users of the social network and expressed in percent. The work of the method is demonstrated on the example of the social network "In contact". This method includes the sequence of the following stages: the first stage is data collection about users of the social network with API and the formation of tuples of users’ profile characteristics. A tuple of characteristics of social network profiles is the data, collected for each user, stored in a structured form. The next step is the analysis of the collected information. For each characteristic of the tuple of profdes, i. e. the possible element of interaction of users in the social network, the coefficient of cohesion by the characteristic is calculated. In addition, for each feature, its informativeness is calculated, i. e. how important is this or that feature in this social network. At the final stage, the results are generated, using the formula for the probability of communication between two users, derived during the investigation. Obtained as a result of the application of the method, the probability of communication between two users can be used to optimize the activities of the operative-search services and special bodies. In addition, the received degree of cohesion of two users can be interpreted as the probability of a channel of information leakage between them. The role of the user of the method can be any private or state organization that cares about the security of corporate data and commercial secrets, the operative-search service, as well as an organization that investigates cybercrimes and information security incidents.
The purpose of research was to study the existing methods of determining the degree of cohesion of two users of social network, identifying their shortcomings and developing a new method. The research identified shortcomings of existing methods and proposed a new method for assessing the degree of cohesion of social network profiles based on open data from a social network. Under the degree of cohesion of users’ profiles is understood the probability of communication (interaction) of profile owners in real life, it is calculated for two users of the social network and expressed in percent. The work of the method is demonstrated on the example of the social network "In contact". This method includes the sequence of the following stages: the first stage is data collection about users of the social network with API and the formation of tuples of users’ profile characteristics. A tuple of characteristics of social network profiles is the data, collected for each user, stored in a structured form. The next step is the analysis of the collected information. For each characteristic of the tuple of profdes, i. e. the possible element of interaction of users in the social network, the coefficient of cohesion by the characteristic is calculated. In addition, for each feature, its informativeness is calculated, i. e. how important is this or that feature in this social network. At the final stage, the results are generated, using the formula for the probability of communication between two users, derived during the investigation. Obtained as a result of the application of the method, the probability of communication between two users can be used to optimize the activities of the operative-search services and special bodies. In addition, the received degree of cohesion of two users can be interpreted as the probability of a channel of information leakage between them. The role of the user of the method can be any private or state organization that cares about the security of corporate data and commercial secrets, the operative-search service, as well as an organization that investigates cybercrimes and information security incidents.
Год издания: 2017
Источник: Открытое образование
Выпуск: № 6
Номера страниц: 14-22
Количество экземпляров:
- Книгохранилище научной литературы (пр. Свободный, 79, 3 этаж): свободно 1 из 1 экземпляров
Ключевые слова: анализ данных, информативность, информационная безопасность, метод накопленных частот, открытые данные, связь профилей социальной сети,, социальные сети
Рубрики: Вычислительная техника,
вычислительные сети
вычислительные сети
ISSN: 1818-4243
Идентификаторы: полочный индекс К 290, шифр otob/2017/6-775676530