title: ÐÐ°ÑÐ¸ÑÐ° Ð°Ð½ÑÐ¸Ð²Ð¸ÑÑÑÐ½ÑÑ ÑÐ¸ÑÑÐµÐ¼ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð¾Ñ ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½ÑÑ Ð°ÑÐ°Ðº
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Defense of neural network based anti-virus systems from adversarial attacks
Creators: name: ÐÐ¾ÑÐ»ÑÑÐ¾Ð²Ð°, ÐÐ½Ð°ÑÑÐ°ÑÐ¸Ñ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2023
Subjects: Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð³Ð»ÑÐ±Ð¾ÐºÐ¸Ðµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½ÑÐµ Ð°ÑÐ°ÐºÐ¸
Ð°Ð½ÑÐ°Ð¼Ð±Ð»ÐµÐ²ÑÐµ ÑÐ¸ÑÑÐµÐ¼Ñ
machine learning
deep neural networks
adversarial attacks
ensemble systems

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description: The topic of the graduate qualification work: Â«Defense of neural network based anti-virus systems from adversarial attacksÂ». The purpose of the study is to increase the robustness of the neural network-based classifier of malicious software to adversarial attacks. The tasks to be solved during this study: 1. Analysis of existing methods for detecting malicious software with neural networks. 2. Analysis of existing methods of conducting adversarial attacks. 3. Suggestion of a method of protecting the neural network-based classifier of malicious software from adversarial attacks. 4. Implementation of a method for protecting the neural network-based classifier of malicious software from adversarial attacks and analysis of the results obtained. As a result of the work, a method of protection based on an ensemble system with a DAE and competitive training was proposed. Attempts have been made to conduct competitive attacks to evaluate the effectiveness of the proposed method in comparison with the method of competitive training and the method based on the use of a DAE. The proposed method showed the best results among the considered approaches. The results obtained can be used as a basis for creating neural networks that must be resistant to adversarial attacks.