title: ÐÐ°ÑÐ¸ÑÐ° Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½Ð¾Ð¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸, Ð²ÑÐ¿Ð¾Ð»Ð½ÑÑÑÐµÐ¹ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ Ð²ÑÐµÐ´Ð¾Ð½Ð¾ÑÐ½Ð¾Ð³Ð¾ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÐµÑÐ¿ÐµÑÐµÐ½Ð¸Ñ, 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° ÑÐ¿ÐµÑÐ¸Ð°Ð»Ð¸ÑÑÐ°
titleType: Subtitle
title: Defence of an artificial neural network that performs the classification of malicious software from adversarial attacks
Creators: name: Ð¡Ð°Ð»Ð¾Ð¼Ð°ÑÐ¸Ð½, ÐÐ°ÐºÑÐ¸Ð¼
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: ÐÐ°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½ÑÐµ Ð¿ÑÐ¸Ð¼ÐµÑÑ
Ð·Ð°ÑÐ¸ÑÐ° Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð´Ð²Ð¸Ð¶ÑÑÐ¸Ñ ÑÑ ÑÐµÐ»ÐµÐ¹
convolutional neural networks
adversarial examples
moving target defense

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description: The theme of the graduate qualification work: Â«Method of malware classification system protection against adversarial attacksÂ». The purpose of the study is to increase the resistance to adversarial attacks of a malicious software detection system that classifies the byte representation of executable files. Tasks to be solved in the course of the study: Analysis of existing methods for detecting malicious software based on artificial neural networks. Analysis of existing methods of conducting adversarial attacks. Analysis of existing defensive methods against adversarial attacks. Implementation of the protection approach of the system performing the HPE classification. Analysis of the results obtained. As a result of the work, an approach was proposed to protect the malware detection system using convolutional neural networks to classify programs from adversarial examples. The developed solution is based on the following methods: the method of protection using moving targets, competitive learning and the fight against competitive learning at the level of the model architecture. The resulting method makes it possible to significantly increase the resistance of systems to attacks using adversarial examples. The work of the proposed approach was demonstrated. The results obtained can be used as a basis for using an approach in malware classification systems without a feature extraction stage requiring domain knowledge.