9004 Problems of information security. Computer systems Проблемы информационной безопасности. Компьютерные системы 2071-8217 5 10.48612/jisp/2741-bb1k-hf3x Detecting adversarial samples in intrusion detection systems using machine learning models Выявление искажающих данных в системах обнаружения вторжений, использующих вычислительные модели машинного обучения Kirillov Roman kirillov.rb@edu.spbstu.ru 0000-0002-9732-0099 Kalinin Maxim max@ibks.spbstu.ru Peter the Great St. Petersburg Polytechnic University 25 03 2025 1 59 68

The problem of protecting machine learning models used in intrusion detection systems from adversarial attacks is considered. Possible methods of protection against adversarial samples based on data anomaly detectors and an autoencoder are analyzed. The results of an experimental study of protective mechanisms that demonstrated high efficiency in detecting distorting data using a Random Forest model are presented.

 Adversarial attack machine learning security adversarial sample detection machine learning intrusion detection system Random Forest