Оценивание информативности признаков в наборах данных для проведения продлённой аутентификации
Ключевые слова:
информативность, классификация, продлённая аутентификация, машинное обучение, отбор признаков, информационная безопасностьАннотация
Продлённая аутентификация позволяет избавиться от недостатков, присущих статической аутентификации, например, идентификаторы могут быть потеряны или забыты, пользователь совершает только первоначальный вход в систему, что может быть опасно не только для областей, требующих обеспечения высокого уровня безопасности, но и для обычного офиса. Динамическая проверка пользователя во время всего сеанса работы может повысить безопасность системы, поскольку во время работы пользователь может подвергнуться воздействию со стороны злоумышленника (например, быть атакованным) или намеренно передать ему права. В таком случае оперировать машиной будет не пользователь, который выполнил первоначальный вход. Классификация пользователей во время работы системы позволит ограничить доступ к важным данным, которые могут быть получены злоумышленником. Во время исследования были изучены методы и наборы данных, использующихся для продлённой аутентификации. Затем был сделан выбор наборов данных, которые использовались в дальнейшем исследовании: данные о движении смартфона и смарт-часов (WISDM) и динамике активности мыши (Chao Shen’s, DFL, Balabit). Помочь улучшить результаты работы моделей при классификации может предварительный отбор признаков, например, через оценивание их информативности. Уменьшение размерности признаков позволяет снизить требования к устройствам, которые будут использоваться при их обработке, повысить объём перебора значений параметров классификаторов при одинаковых временных затратах, тем самым потенциально повысить долю правильных ответов при классификации за счёт более полного перебора параметров значений. Для оценивания информативности использовались метод Шеннона, а также алгоритмы, встроенные в программы для анализа данных и машинного обучения (WEKA: Machine Learning Software и RapidMiner). В ходе исследования были выполнены расчёты информативности каждого признака в выбранных для исследования наборах данных, затем с помощью RapidMiner были проведены эксперименты по классификации пользователей с последовательным уменьшением количества используемых при классификации признаков с шагом в 20%. В результате была сформирована таблица с рекомендуемыми наборами признаков для каждого набора данных, а также построены графики зависимостей точности и времени работы различных моделей от количества используемых при классификации признаков.
Литература
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17. Deb D., Ross A., Jain A.K., Prakah-Asante K., Prasad K.V. Actions speak louder than (pass) words: Passive authentication of smartphone* users via deep temporal features // International conference on biometrics (ICB). 2019. pp. 1–8.
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19. Barlas Y., Basar O.E., Akan Y., Isbilen M., Alptekin G.I., Incel O.D. DAKOTA: Continuous authentication with behavioral biometrics in a mobile banking application // 5th International Conference on Computer Science and Engineering (UBMK). 2020. pp. 1–6.
20. Mekruksavanich S., Jitpattanakul A. Deep learning approaches for continuous authentication based on activity patterns using mobile sensing // Sensors. 2021. vol. 21. no. 22. DOI: 10.3390/s21227519.
21. Acien A., Morales A., Vera-Rodriguez R., Fierrez J., Tolosana R.. Multilock: Mobile active authentication based on multiple biometric and behavioral patterns // 1st International Workshop on Multimodal Understanding and Learning for Embodied Applications. 2019. pp. 53–59.
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Опубликован
2024-01-11
Как цитировать
Давыденко, С. А., Костюченко, Е. Ю., & Новиков, С. Н. (2024). Оценивание информативности признаков в наборах данных для проведения продлённой аутентификации. Информатика и автоматизация, 23(1), 65-100. https://doi.org/10.15622/ia.23.1.3
Раздел
Искусственный интеллект, инженерия данных и знаний
Copyright (c) Сергей Андреевич Давыденко
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