Аннотация: The subject of the study is to identify effective methods of legislative work to counteract the use by telephone fraudsters of such technical means as illegal substitution of SIM cards and Internet services for substitution of incoming call numbers. The general scientific methodology of dialectical (meaningful) logic and comparative analysis of practical problems and legislative activity of federal authorities are used. Fraud causes huge damage to society and incurs huge costs to the state. The global spread of the Internet has allowed scammers to export their activities to a fast-growing market and attract previously untapped consumers. The evolution of technologies and the spread of fraudulent approaches on the Internet have exacerbated the problems faced by victims. The results serve as evidence that when detecting and timely stopping attempts at telephone fraud (suspending suspicious transactions), legislative support and the formation of a subordinate regulatory framework are necessary for the interaction of financial organizations, telecommunications operators and law enforcement agencies. The development of smartphones and cellular networks increases the need for mobile advertising and targeted marketing. However, it also causes invisible security threats. We have found that phone fraud with fake phone numbers with a very short service life is becoming more and more popular and is being used to deceive users. The article is devoted to the consideration of the problem of legal regulation to ensure information security. As phone fraud becomes more common, it is extremely important to understand how to increase the effectiveness of prevention. Conclusions are drawn about the need to strengthen the centralization of countering intruders in order to increase the effectiveness of preventing telephone fraud, following the example of creating an interbank digital platform "Know your Customer".

Аннотация: The subject of the study is the problem of legal prevention of the use of computer and telecommunication technologies by intruders in new financial remote services in Russia. An increase in the variety and volume of attacks is inevitable, given the desire of scammers to obtain personal and confidential information. In recent years, Russia has made significant progress in improving its infrastructure responsible for information security. The article is a comprehensive analysis of Russian legislation. The analytical review of various directions of development of the Russian federal legislation in recent years aimed at preventive counteraction, elimination of a number of conditions and prerequisites of cybercrime in the financial sphere is presented. Particular attention is paid to the jurisdictional aspects of Russian legislation. The government needs to make thorough preparations to counter a range of unwanted cyber events, both accidental and intentional. There are significant risks of local attacks and losses as a result of compromising computer and telecommunications services. The conclusions contain final proposals for further improvement of legislation taking into account foreign and international experience. The main conclusions of the study are the productivity of identifying the strategic prevention direction in preventive activities – preventive identification and elimination of gaps in the regulatory framework, as well as technical and organizational vulnerabilities that make possible various types of attacks and "schemes" of cybercriminals in the financial sphere.

Аннотация: Достижения в области коммуникационных технологий сделали общение между людьми более доступным. В эпоху информационных технологий обмен информацией стал очень простым и быстрым. Однако личная и конфиденциальная информация может быть доступна в Интернете. Например активно применяется злоумышленниками голосовой фишинг, Вред от вишинга является серьезной проблемой во всем мире, и его частота растет. Системы связи уязвимы и могут быть легко взломаны злоумышленниками с помощью атак социальной инженерии. Эти атаки направлены на то, чтобы обманом заставить людей или предприятия выполнять действия, которые приносят пользу злоумышленникам, или предоставить им конфиденциальные данные. В этой статье исследуется полезность применения различных подходов к обучению для решения проблемы обнаружения мошенничества в сфере телекоммуникаций. Голос человека содержит различные параметры, которые передают информацию, такую как эмоции, пол, отношение, здоровье и личность. Технологии распознавания говорящих имеют широкие области применения, в частности противодействие телефонному мошенничеству. Распознавание эмоций становится все более актуальной технологией так же с развитием систем голосовых помощников. Одной из целей исследования является определение пользовательской модели, которая лучше всего выявляет случаи мошенничества. Машинное обучение обеспечивают эффективные технологии для обнаружения мошенничества и успешно применяются для обнаружения таких действий, как фишинг, кибербуллинг, мошенничество в сфере телекоммуникаций.

Abstract: Advances in communication technologies have made communication between people more accessible. In the era of information technology, information exchange has become very simple and fast. However, personal and confidential information may be available on the Internet. For example, voice phishing is actively used by intruders. The harm from phishing is a serious problem all over the world, and its frequency is growing. Communication systems are vulnerable and can be easily hacked by attackers using social engineering attacks. These attacks are aimed at tricking people or businesses into performing actions that benefit attackers, or providing them with confidential data. This article explores the usefulness of applying various approaches to training to solve the problem of fraud detection in telecommunications. A person's voice contains various parameters that convey information such as emotions, gender, attitude, health and personality. Speaker recognition technologies have wide areas of application, in particular countering telephone fraud. Emotion recognition is becoming an increasingly relevant technology as well with the development of voice assistant systems. One of the goals of the study is to determine the user model that best identifies fraud cases. Machine learning provides effective technologies for fraud detection and is successfully used to detect such actions as phishing, cyberbullying, and telecommunications fraud.

Аннотация: В настоящее время генерируется огромное количество текстов, и существует острая необходимость организовать их в определенной структуре, для выполнения классификации и правильного определения категорий. Авторы подробно рассматривают такие аспекты темы как классификация текстов на естественном языке и определение тональности текста в социальной сети Twitter. Использование социальных сетей помимо многочисленных плюсов, несет и негативный характер, а именно пользователи сталкиваются с многочисленными киберугрозами, такими как утечка персональных данных, кибербуллинг, спам, фейковые новости. Основной задачей анализа тональности текста является определение эмоциональной наполненности и окраски, что позволит выявить негативно окрашенную тональность речи. Эмоциональная окраска или настроение являются сугубо индивидуальными чертами и, таким образом, несут потенциал в качестве инструментов идентификации.   Основная цель классификации текста на естественном языке состоит в том, чтобы извлекать информацию из текста и использовать такие процессы, как поиск, классификация с применением методов машинного обучения. Авторы отдельно выбрали и сравнили следующие модели: логистическая регрессия, многослойный перцептрон, случайный лес, наивный байесовский метод, метод K-ближайших соседей, дерево решений и стохастический градиентный спуск. Затем мы протестировали и проанализировали эти методы друг с другом. Экспериментальный вывод показывает, что применение скоринга TF-IDF для векторизации текста улучшает качество модели не всегда, либо делает это для отдельных метрик, вследствие чего уменьшается показатель остальных метрик для той или иной модели. Наилучшим методом для выполнения цели работы является Стохастический градиентный спуск.

Abstract: Currently, a huge number of texts are being generated, and there is an urgent need to organize them in a certain structure in order to perform classification and correctly define categories. The authors consider in detail such aspects of the topic as the classification of texts in natural language and the definition of the tonality of the text in the social network Twitter. The use of social networks, in addition to numerous advantages, also carries a negative character, namely, users face numerous cyber threats, such as personal data leakage, cyberbullying, spam, fake news. The main task of the analysis of the tonality of the text is to determine the emotional fullness and coloring, which will reveal the negatively colored tonality of speech. Emotional coloring or mood are purely individual traits and thus carry potential as identification tools. The main purpose of natural language text classification is to extract information from the text and use processes such as search, classification using machine learning methods. The authors separately selected and compared the following models: logistic regression, multilayer perceptron, random forest, naive Bayesian method, K-nearest neighbor method, decision tree and stochastic gradient descent. Then we tested and analyzed these methods with each other. The experimental conclusion shows that the use of TF-IDF scoring for text vectorization does not always improve the quality of the model, or it does it for individual metrics, as a result of which the indicator of the remaining metrics for a particular model decreases. The best method to accomplish the purpose of the work is Stochastic gradient descent.

Аннотация: The authors consider in detail the identification of cyberbullying, which is carried out by fraudsters with the illegal use of the victim's personal data. Basically, the source of this information is social networks, e-mails. The use of social networks in society is growing exponentially on a daily basis. The use of social networks, in addition to numerous advantages, also has a negative character, namely, users face numerous cyber threats. Such threats include the use of personal data for criminal purposes, cyberbullying, cybercrime, phishing and cyberbullying. In this article, we will focus on the task of identifying trolls. Identifying trolls on social networks is a difficult task because they are dynamic in nature and are collected in several billion records. One of the possible solutions to identify trolls is the use of machine learning algorithms. The main contribution of the authors to the study of the topic is the use of the method of identifying trolls in social networks, which is based on the analysis of the emotional state of network users and behavioral activity. In this article, in order to identify trolls, users are grouped together, this association is carried out by identifying a similar way of communication. The distribution of users is carried out automatically through the use of a special type of neural networks, namely self-organizing Kohonen maps. The group number is also determined automatically. To determine the characteristics of users, on the basis of which the distribution into groups takes place, the number of comments, the average length of the comment and the indicator responsible for the emotional state of the user are used.