9004 Problems of information security. Computer systems Проблемы информационной безопасности. Компьютерные системы 2071-8217 12 10.48612/jisp/hmuz-b8ua-mv2e Comparison of the effectiveness of anomaly detection by machine learning algorithms without a teacher Сравнение эффективности выявления аномалий алгоритмами машинного обучения без учителя 0000-0003-0623-9891 Gololobov Nikita gololobov_nv@spbstu.ru 0000-0003-1345-1874 Pavlenko Evgeny pavlenko_eyu@spbstu.ru Peter the Great St. Petersburg Polytechnic University 10 06 2022 2 135 147

The paper proposes the use of recurrent neural networks with the LSTM architecture for solving problems related to the detection of anomalous instances in data sets and compares the effectiveness of the proposed method with the traditional technique – the support vector machine for one class. During the study, an experiment was conducted and criteria for the effectiveness of implementations were formulated. The results obtained in this way made it possible to draw appropriate conclusions about the applicability of recurrent neural networks in the tasks of detecting anomalous instances and put forward proposals for the further development of this direction

 anomaly detection machine learning support vector method recurrent neural networks LSTM learning without a teacher recurrent neural networks