title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¸ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð¿Ð¾Ð²ÑÑÐµÐ½Ð¸Ñ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ ÑÐ°Ð·Ð·Ð¸Ð½Ð³-ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¸Ð½ÑÐµÑÑÐµÐ¹ÑÐ¾Ð² Ð¿ÑÐ¸ÐºÐ»Ð°Ð´Ð½Ð¾Ð³Ð¾ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Development and research of machine learning methods to improve the efficiency of fuzzing testing of application programming interfaces
Creators: name: Ð©ÑÑ, Ð¡Ð°Ð²ÐµÐ»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: 2023
Subjects: ÑÐ°Ð·Ð·Ð¸Ð½Ð³-ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
rest api
Ð²Ð°ÑÐ¸Ð°ÑÐ¸Ð¾Ð½Ð½ÑÐ¹ Ð°Ð²ÑÐ¾ÐºÐ¾Ð´Ð¸ÑÐ¾Ð²ÑÐ¸Ðº
fuzz testing
variational autoencoder

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description: This work is devoted to the study of existing problems in fuzzing testing of application programming interfaces, the development of our own framework and the study of the use of variational autoencoder to generate new data for testing. Tasks that were solved during the work: 1. Development of a flexible framework for fuzzing testing, containing a more customizable parameters. 2. Testing the created framework on a real application, comparing the results with other frameworks for fuzz testing. 3. Creation of variational autoencoders for generating new initial data. 4. A research of the efficiency of fuzzing with new initial data. As a result of work, created a new tool for the introduction of fuzzing testing, researched the variational autoencoder algorithm for generating new data for fuzzing, and described practical recommendations for the introduction and use of developments in real projects.