title: ÐÐ±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ðµ Ð²ÑÐµÐ´Ð¾Ð½Ð¾ÑÐ½Ð¾Ð¹ Ð»Ð¾Ð³Ð¸ÐºÐ¸ Ð² ÑÐ°Ð±Ð¾ÑÐµ ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÑ Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð² Ð²Ñ Ð¾Ð´Ð½ÑÑ Ð´Ð°Ð½Ð½ÑÑ
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: Detecting of malicious logic in the operation of convolutional artificial neural networks based on the analysis of input data features
Creators: name: ÐÐ°ÐºÐ°ÑÐ¾Ð², 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¸
Ð¿Ð¾Ð¸ÑÐº ÑÑÐ·Ð²Ð¸Ð¼Ð¾ÑÑÐµÐ¹
ÑÐºÑÑÑÐ°Ñ Ð»Ð¾Ð³Ð¸ÐºÐ°
Ð¿Ð¾ÑÐ°Ð¹Ð½ÑÐµ Ñ Ð¾Ð´Ñ
Ð²ÐµÐ¹Ð²Ð»ÐµÑ-Ð¿ÑÐµÐ¾Ð±ÑÐ°Ð·Ð¾Ð²Ð°Ð½Ð¸Ðµ
vulnerability scanning
backdoor
malicious logic
wavelet transform

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description: The topic of the graduate qualification work is Â«Detecting of malicious logic in the operationÂ of convolutional artificial neural networks based on the analysis of input data featuresÂ». The subject of the study is the protection of classifiers based on artificial neural networks from the introduction of secret passages that activate malicious logic. The purpose of the study is protect convolutional artificial neural networks from malicious logic attacks implementation. The research set the following goals: Research of theoretical ways to implement malicious logic implementation attacks. Analysis of modern research in the field of search for malicious logic in convolutional neural networks.Investigation of ways to compress images to remove features. Development of a method for searching for malicious logic based on compression using a wavelet transform. Evaluation of the quality of the created method of searching for malicious logic. As a result of the work, a means of combating malicious logic in the operation of convolutional neural networks was developed, and the effectiveness of its work was demonstrated. It was concluded that lossy compression algorithms can be appliedÂ to input data to combat malicious logic in the operation of convolutional artificial neural networks. The results obtained can be used as a basis for designing systems for dynamic search of malicious logic in the operation of convolutional neural networks.