title: Ð¤Ð°Ð·Ð·Ð¸Ð½Ð³ Ð´Ð°Ð½Ð½ÑÑ Ð´Ð»Ñ ÑÐ¸ÑÑÐµÐ¼ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð²ÑÐ¾ÑÐ¶ÐµÐ½Ð¸Ð¹ Ð¿ÑÐ¸ Ð¿Ð¾Ð¼Ð¾ÑÐ¸ Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹
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: Fuzzing data for intrusion detection systems using artificial neural networks
Creators: name: ÐÐ¾ÑÐºÐ°Ð»ÐµÐ², ÐÐµÐ¾ÑÐ³Ð¸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: 2022
Subjects: ÑÐ°Ð·Ð·Ð¸Ð½Ð³
Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ñ Ð´Ð°Ð½Ð½ÑÑ
Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÑÐ¸ÑÑÐµÐ¼Ð° Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð²ÑÐ¾ÑÐ¶ÐµÐ½Ð¸Ð¹
fuzzing
data generation
neural networks
intrusion detection system

description: ÐÑÐµÐ´Ð¼ÐµÑÐ¾Ð¼ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÐ²Ð»ÑÐµÑÑÑ ÑÐ°Ð·Ð·ÐµÑ ÑÐµÑÐµÐ²ÑÑ Ð´Ð°Ð½Ð½ÑÑ Ð¸ Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½Ð°Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ Ð´Ð»Ñ Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð¸ Ð´Ð°Ð½Ð½ÑÑ. Ð¦ÐµÐ»ÑÑ ÑÐ°Ð±Ð¾ÑÑ ÑÐ²Ð»ÑÐµÑÑÑ ÑÐ»ÑÑÑÐµÐ½Ð¸Ðµ ÐºÐ°ÑÐµÑÑÐ²Ð° ÑÐ¸ÑÑÐµÐ¼ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð²ÑÐ¾ÑÐ¶ÐµÐ½Ð¸Ð¹ Ð¿ÑÑÐµÐ¼ ÑÐ°Ð·Ð·Ð¸Ð½Ð³Ð° ÑÐµÑÑÐ¾Ð²ÑÑ Ð´Ð°Ð½Ð½ÑÑ. ÐÐ°Ð´Ð°ÑÐ¸, ÐºÐ¾ÑÐ¾ÑÑÐµ ÑÐµÑÐ°Ð»Ð¸ÑÑ Ð² ÑÐ¾Ð´Ðµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ: â Ð¸Ð·ÑÑÐµÐ½Ð¸Ðµ Ð¸ Ð°Ð½Ð°Ð»Ð¸Ð· ÑÐ°Ð·Ð½ÑÑ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð¾Ð² Ð´Ð»Ñ ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ñ ÑÐ°Ð·Ð·ÐµÑÐ¾Ð²; â Ð¸Ð·ÑÑÐµÐ½Ð¸Ðµ ÑÐ°Ð·Ð½Ð¾Ð²Ð¸Ð´Ð½Ð¾ÑÑÐµÐ¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹; â ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ Ð¿ÑÐ¾ÑÐ¾ÑÐ¸Ð¿Ð° ÑÐ°Ð·Ð·ÐµÑÐ° Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð¸ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹; â Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ ÑÐ³ÐµÐ½ÐµÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð½ÑÑ Ð´Ð°Ð½Ð½ÑÑ Ð² Ð¡ÐÐ Ð´Ð»Ñ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ñ ÐºÐ°ÑÐµÑÑÐ²Ð° ÐµÐµ Ð½Ð°ÑÑÑÐ¾Ð¹ÐºÐ¸. ÐÑÐ¾Ð²ÐµÐ´ÐµÐ½ ÑÑÐ°Ð²Ð½Ð¸ÑÐµÐ»ÑÐ½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð· Ð¼ÐµÑÐ¾Ð´Ð¾Ð² ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ, Ð²ÑÑÐ²Ð»ÐµÐ½Ñ Ð´Ð¾ÑÑÐ¾Ð¸Ð½ÑÑÐ²Ð° Ð¸ Ð½ÐµÐ´Ð¾ÑÑÐ°ÑÐºÐ¸ ÑÐ°Ð·Ð·Ð¸Ð¸Ð½Ð³Ð° Ð¿Ð¾ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ñ Ñ Ð´ÑÑÐ³Ð¸Ð¼Ð¸ Ð¼ÐµÑÐ¾Ð´Ð°Ð¼Ð¸, Ð° ÑÐ°ÐºÐ¶Ðµ ÑÐ°Ð·Ð¾Ð±ÑÐ°Ð½Ñ ÑÐ°Ð·Ð½ÑÐµ Ð¿Ð¾Ð´ÑÐ¾Ð´Ñ Ðº ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ñ ÑÐ°Ð·Ð·ÐµÑÐ¾Ð². Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ ÑÐ°Ð±Ð¾ÑÑ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½ ÑÐ°Ð·Ð·ÐµÑ ÑÐµÑÐ½Ð¾Ð³Ð¾ ÑÑÐ¸ÐºÐ° Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð¸ Ð´Ð°Ð½Ð½ÑÑ. Ð ÐºÐ°ÑÐµÑÑÐ²Ðµ Ð³ÐµÐ½ÐµÑÐ°ÑÐ¾ÑÐ¾Ð² Ð´Ð°Ð½Ð½ÑÑ, ÐºÐ¾ÑÐ¾ÑÑÐµ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÑÑÑÑ Ð´Ð»Ñ ÑÐ°Ð·Ð½ÑÑ ÑÐ¸Ð¿Ð¾Ð² Ð°ÑÐ°Ðº, Ð¿ÑÐ¸Ð¼ÐµÐ½ÑÐ»Ð¸ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸ Ð½Ð° Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÐµ LSTM Ð¸ GAN, Ð¾Ð±ÑÑÐµÐ½Ð½ÑÐµ Ð½Ð° Ð½Ð°Ð±Ð¾ÑÐµ Ð´Ð°Ð½Ð½ÑÑ UNSW-NB15. ÐÐ»Ñ ÑÐ¾ÐºÑÐ°ÑÐµÐ½Ð¸Ñ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð² Ð°ÑÐ°Ðº Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð»ÑÑ Ð¼ÐµÑÐ¾Ð´ Ð³Ð»Ð°Ð²Ð½ÑÑ ÐºÐ¾Ð¼Ð¿Ð¾Ð½ÐµÐ½Ñ. ÐÐ¾Ð»ÑÑÐµÐ½Ð½ÑÐ¹ Ð¸Ð½ÑÑÑÑÐ¼ÐµÐ½Ñ Ð¼Ð¾Ð¶ÐµÑ Ð±ÑÑÑ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½ Ð´Ð»Ñ ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÐµÑÐ¿ÐµÑÐµÐ½Ð¸Ñ.
description: The subject of the research is a network data fuzzer and an artificial neural network for data generation. The aim of the work is to improve the quality of intrusion detection systems by fuzzing test data. Tasks that were solved in the course of the study: â study and analysis of different approaches for creating fuzzers; â study of types of neural networks; â implementation of a fuzzer prototype based on generation using neural networks; â application of the generated data in the IDS to determine the quality of its settings. A comparative analysis of testing methods was carried out, the advantages and disadvantages of fuzzing compared to other methods were identified, and different approaches to creating fuzzers were analyzed. As a result of the work, a black box fuzzer was implemented using data generation. LSTM and GAN architecture neural networks trained on the UNSW-NB15 dataset were used as data generators that are used for different types of attacks. To reduce attack parameters, the principal component analysis was used. The resulting tool can be used for software testing.