Алгоритм классификации псевдослучайных последовательностей
Ключевые слова:
статистический анализ данных, машинное обучение, классификация бинарных последовательностей, DLP системы, защита информации от утечки
Аннотация
В последнее время увеличилось количество утечек информации, произошедших по вине внутренних нарушителей, одной из возможных причин может являться не способность современных DLP систем противостоять утечкам информации в зашифрованном или сжатом виде. Был предложен алгоритм классификации последовательностей, сформированных алгоритмами шифрования, сжатия и генераторами псевдослучайных чисел. Для решения задачи классификации предлагается использовать методы машинного обучения на основе алгоритма построения дерева решений. В качестве признакового пространства использовался массив частот встречаемости двоичных подпоследовательностей длины N бит. При построении признакового пространства не использовались заголовки файлов или какая-либо другая контекстная информация. Был обоснован выбор гиперпараметров классификатора. Представленный алгоритм показал точность классификации указанных в работе последовательностей 0.98. Представленный алгоритм может быть реализован в DLP системах для предотвращения передачи информации в зашифрованном или сжатом виде.
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Литература
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5. Alneyadi S., Sithirasenan E., Muthukkumarasamy V. Discovery of potential data leaks in email communications. 2016 10th International Conference on Signal Processing and Communication Systems (ICSPCS). Gold Coast, Australia, 2016. P. 1–10. DOI
6. Huang X., Lu Y., Li D., Ma M. A novel mechanism for fast detection of transformed data leakage. IEEE Access. 2018. V. 6. P. 35926–35936. DOI
7. Kaur K., Gupta I., Singh A. K. A Comparative Evaluation of Data Leakage/Loss prevention Systems (DLPS). In Proc. 4th Int. Conf. Computer Science & Information Technology (CS & IT-CSCP). 2017. P. 87–95. DOI
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31. Archive manager WinRAR. Available at: URL (accessed: 14.01.2020).
32. Linux programmer’s manual. Available at: URL (accessed: 14.01.2020).
33. Programm environment Anaconda. Available at: URL (accessed: 14.01.2020).
34. Breiman, L. Classification and regression trees. Routledge, 2017. 358 p.
Опубликован
2020-03-24
Как цитировать
Козачок, А. В., & Спирин, А. А. (2020). Алгоритм классификации псевдослучайных последовательностей. Вестник ВГУ. Серия: Системный анализ и информационные технологии, (1), 87-98. https://doi.org/10.17308/sait.2020.1/2595
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