title: ÐÐ°ÑÐ¸ÑÐ° ÑÐµÑÐµÐ²Ð¾Ð³Ð¾ ÑÑÐ°ÑÐ¸ÐºÐ° Ð¾Ñ ÑÐ³ÑÐ¾Ð·Ñ Ð¸Ð´ÐµÐ½ÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÐµÐ»ÑÑÐºÐ¸Ñ Ð¿ÑÐ¸Ð»Ð¾Ð¶ÐµÐ½Ð¸Ð¹
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: Protection of network traffic from user-end app identification threats
Creators: name: Ð¡ÑÐ´Ð°ÐºÐ¾Ð², ÐÐ¼Ð¸ÑÑÐ¸Ð¹
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: 2025
Subjects: ÑÐµÑÐµÐ²Ð¾Ð¹ ÑÑÐ°ÑÐ¸Ðº
Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ
Ð¸Ð´ÐµÐ½ÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ Ð¿ÑÐ¸Ð»Ð¾Ð¶ÐµÐ½Ð¸Ð¹
Ð¾Ð±ÑÑÑÐºÐ°ÑÐ¸Ñ
Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ Ð±ÐµÐ·Ð¾Ð¿Ð°ÑÐ½Ð¾ÑÑÑ
ÐºÐ¾Ð½ÑÐ¸Ð´ÐµÐ½ÑÐ¸Ð°Ð»ÑÐ½Ð¾ÑÑÑ
network traffic
deep learning
classification
user-end app identification
confidentiality
obfuscation

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description: The topic of the graduate qualification work is Â«Protection of network traffic from user-end app identification threatsÂ». The purpose of the study is to enhance the resilience of network traffic against privacy attacks carried out through the identification of user applications using deep learning neural networks. The subject of the work is the protection of network traffic from the threat of user-end app identification. The research set the following goals: 1. Analyze modern research addressing privacy threats in network interactions. 2. Describe adversary model. 3. Develop an algorithm to enhance the resilience of network traffic to the specified type of attacks. 4. Implement the proposed algorithm in software. 5. Evaluate the effectiveness of the developed software The study examined the threat to the confidentiality of network interactions posed by the use of deep learning neural networks. Modern research in a network security was analyzed. A software solution was developed to reduce the accuracy of classifying outgoing network traffic by user applications. A comparative analysis of metrics used to evaluate classifying neural networks confirmed the effectiveness of the proposed solution. It was found that the examined threat remains underexplored. The results of this work can serve as a foundation for the development of systems aimed at enhancing the security of network interactions.