ВЫБОР РАЗМЕРА КОДОВОЙ ТАБЛИЦЫ В МОДЕЛИ ЭЛЕКТРОННЫХ ПИСЕМ
Authors: Корелов С.В. , Петров А.М. , Сидоркина И.Г. , Ротков Л.Ю. , Горбунов А.А. 
Published: February 2025

Функционирование практически любой организации в той или иной степени зависит от того, насколько надёжно защищены её информационные ресурсы от различных угроз безопасности информации, одной из которых является спам. Совершено множество попыток раз и навсегда решить проблему его обнаружения. В данной предметной области постоянно ведутся исследования. По их результатам предлагаются и реализуются на практике различные подходы. Ранее авторами предложена модель электронных писем, учитывающая их содержание, которое зачастую меняется в зависимости от выполняемых пользователями задач и меняющихся их информационных потребностей. В настоящей статье обсуждается вопрос выбора численного значения ключевого параметра модели электронных писем для обнаружения спама, полученной на основе генетического подхода к формированию математических моделей текстов, зарекомендовавшего себя для решения различных задач.
Introduction. Currently, one of the most widespread methods of everyday and business communication and management is electronic mail, which has a lot of benefits. In addition to this, e-mail is a mandatory requirement for the use of various digital services, including government one. One of the typical risks associated with its use is spam. It can be reasonably assumed that there has emerged a fairly robust and comprehensive system of methods, models and facilities of spam detection. However, existing approaches to spam detection, typically, do not take into account the changing information needs of a particular user and are not aimed at identifying legitimate messages, which leads to false alarms. Thus, the creation of new models of e-mails that detect the traits of e-mail messages based on their content, taking into account the changing information needs of a particular user (personalization), for the detection of anonymous unsolicited bulk mailings is topical and exhibits a scientific and practical interest. The aim of the research is to choose the value q by carrying out experimental studies. Experiment and evaluation of findings. For each category (class) of mails (legal and spam) of each group of emails, we calculated the sets of terms and determined the coefficient of appertain of each email to legal or spam category, which exhibits the total number of terms contained in the email that were found in the corresponding categories of all groups. The decision on whether an email belongs to spam or legal was made with the use of the simple decision rule - according to the largest total number of terms in the corresponding category. In the case of an equal number of terms for both categories, the email was noted as unclassified. The experiment was carried out on a set of English-language emails, consisting of 6 groups of legal emails with a total amount of 16,100 and 6 groups of spam emails with a total amount of 16,420. There was also used in experiment a set of Russian-language emails, consisting of 3 groups of legal emails with a total amount of 1,242 and 2 groups of spam emails with a total amount of 3,215. Conclusion. The study of the influence of the size of the code chart on the classification of emails showed that the use of the model outputs the best results of the classification of emails with q equal to the number of codes (in the study, q = 256 for the ASCII chart), which are compared to the characters of the text in specific encoding (full code chart, providing the maximum variety of texts), and n = 1. At the same time, depending on the symbolic content of the emails, there are cases when the best result can be observed for n = 2 and other different values ​​of q, which allows to adapt (train) the model to specific texts. This indicates that the model allows to distinguish informative features of emails, taking into account the content of emails of a specific user, which allows to meet personalization of the spam detection process as one of the key properties of spam detection systems, as well as to increase its efficiency. Outcome.There was proposed a model of emails, which allows to specifically detect text segments of emails, which are a reflection of their distinctive features, or terms. The findings indicate the customizability of the model in relation to specific texts of emails, as well as the non-random behavior of the obtained results.