title: ÐÐ¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ðµ Ð·Ð½Ð°ÑÐµÐ½Ð¸Ð¹ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð² Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð´Ð»Ñ Ð·Ð°ÑÐ¸Ñ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° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Determination of neural network model parameter values for defense against adversarial attacks
Creators: name: ÐÐ±Ð¸Ð´Ð¸Ð½Ð°, ÐÐ»ÐµÐºÑÐ°Ð½Ð´ÑÐ°
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Ð¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½ÑÐµ Ð°ÑÐ°ÐºÐ¸
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð¼ÐµÑÐ¾Ð´ Ð³Ð»Ð°Ð²Ð½ÑÑ ÐºÐ¾Ð¼Ð¿Ð¾Ð½ÐµÐ½Ñ
Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ðµ ÑÐµÑÐµÐ²ÑÑ Ð°ÑÐ°Ðº
adversarial attacks
machine learning
principal component analysis
network attack detection

description: Ð¢ÐµÐ¼Ð° Ð²ÑÐ¿ÑÑÐºÐ½Ð¾Ð¹ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ: Â«ÐÐ¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ðµ Ð·Ð½Ð°ÑÐµÐ½Ð¸Ð¹ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð² Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð´Ð»Ñ Ð·Ð°ÑÐ¸ÑÑ Ð¾Ñ ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½ÑÑ Ð°ÑÐ°ÐºÂ».Ð¦ÐµÐ»ÑÑ ÑÐ°Ð±Ð¾ÑÑ ÑÐ²Ð»ÑÐµÑÑÑ Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð¸Ðµ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ ÑÐµÑÐµÐ²ÑÑ Ð°ÑÐ°Ðº, ÑÑÑÐ¾Ð¹ÑÐ¸Ð²ÑÑ Ðº ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½ÑÐ¼ Ð°ÑÐ°ÐºÐ°Ð¼. ÐÑÐµÐ´Ð¼ÐµÑÐ¾Ð¼ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÐ²Ð»ÑÑÑÑÑ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¿Ð¾Ð»Ð½Ð¾ÑÐ²ÑÐ·Ð½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð¿ÑÑÐ¼Ð¾Ð³Ð¾ ÑÐ°ÑÐ¿ÑÐ¾ÑÑÑÐ°Ð½ÐµÐ½Ð¸Ñ. ÐÐ°Ð´Ð°ÑÐ¸, ÐºÐ¾ÑÐ¾ÑÑÐµ ÑÐµÑÐ°Ð»Ð¸ÑÑ Ð² ÑÐ¾Ð´Ðµ Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð¸Ñ Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ: ÐÐ½Ð°Ð»Ð¸Ð· Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹. ÐÑÐ±Ð¾Ñ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸. ÐÐ¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ðµ Ð·Ð½Ð°ÑÐ¸Ð¼ÑÑ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð² Ð´Ð»Ñ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ ÑÐµÑÐµÐ²ÑÑ Ð°ÑÐ°Ðº. ÐÐ¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ðµ Ð½Ð°Ð¸Ð±Ð¾Ð»ÐµÐµ ÑÑÐ¾Ð¹ÐºÐ¾Ð¹ Ðº ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½ÑÑ Ð°ÑÐ°ÐºÐ°Ð¼ Ð¼Ð¾Ð´ÐµÐ»Ð¸. Ð Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ Ð±ÑÐ»Ð¸ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ñ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÐµ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÑ Ð¿Ð¾Ð»Ð½Ð¾ÑÐ²ÑÐ·Ð½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð¿ÑÑÐ¼Ð¾Ð³Ð¾ ÑÐ°ÑÐ¿ÑÐ¾ÑÑÑÐ°Ð½ÐµÐ½Ð¸Ñ, ÑÐ°ÐºÐ¸Ðµ ÐºÐ°Ðº ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ð¾ Ð½ÐµÐ¹ÑÐ¾Ð½Ð¾Ð² Ð² ÑÐºÑÑÑÑÑ ÑÐ»Ð¾ÑÑ, ÑÑÐ½ÐºÑÐ¸Ð¸ Ð°ÐºÑÐ¸Ð²Ð°ÑÐ¸Ð¸, Ð¼ÐµÑÐ¾Ð´Ñ Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ð¸, Ð¿Ð¾Ð´ÑÐ¾Ð´Ñ Ðº Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹. Ð¢Ð°ÐºÐ¶Ðµ Ð±ÑÐ»Ð¸ Ð¸Ð·ÑÑÐµÐ½Ñ Ð¿Ð¾Ð½ÑÑÐ¸Ðµ ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½Ð¾Ð¹ Ð°ÑÐ°ÐºÐ¸ Ð¸ ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½Ð¾Ð³Ð¾ Ð¿ÑÐ¸Ð¼ÐµÑÐ°, Ð±Ð°Ð·Ð° Ð´Ð°Ð½Ð½ÑÑ ÑÐµÑÐµÐ²ÑÑ Ð°ÑÐ°Ðº UNSW-NB 15.Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð¸ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ñ Ð·Ð½Ð°ÑÐ¸Ð¼ÑÐµ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¸ Ð±Ð°Ð·Ñ Ð´Ð°Ð½Ð½ÑÑ ÑÐµÑÐµÐ²ÑÑ Ð°ÑÐ°Ðº UNSW-NB 15, Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ ÐºÐ¾ÑÐ¾ÑÑÑ Ð±ÑÐ»Ð¸ Ð¿ÑÐµÐ´Ð¿ÑÐ¸Ð½ÑÑÑ Ð¿Ð¾Ð¿ÑÑÐºÐ¸ Ð¿ÑÐ¾Ð²ÐµÑÑÐ¸ ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½ÑÐµ Ð°ÑÐ°ÐºÐ¸ Ð½Ð° ÑÐ¾Ð·Ð´Ð°Ð½Ð½ÑÐµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¿ÐµÑÑÐµÐ¿ÑÑÐ¾Ð½Ð¾Ð². Ð¡ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ñ Ð¼ÐµÑÑÐ¸Ðº Ð¸Ð· Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð±ÑÐ»Ð° Ð²ÑÐ±ÑÐ°Ð½Ð° Ð»ÑÑÑÐ°Ñ. ÐÐ¾Ð»ÑÑÐµÐ½Ð½ÑÐµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¼Ð¾Ð³ÑÑ Ð±ÑÑÑ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ñ Ð´Ð»Ñ ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ñ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹, ÐºÐ¾ÑÐ¾ÑÑÐµ Ð´Ð¾Ð»Ð¶Ð½Ñ Ð±ÑÑÑ Ð·Ð°ÑÐ¸ÑÐµÐ½Ñ Ð¾Ñ ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½ÑÑ Ð°ÑÐ°Ðº.
description: The topic of the graduate qualification work is Â«Determination of neural network model parameter values for defense against adversarial attacksÂ».The purpose of the study is to build models for detecting network attacks that are resistant to adversarial attacks. Studying inner working of Linux-family OS kernel network stack. The tasks that were solved during the execution of this study:Analysis of neural networks.Choice of neural network architecture.3. Â Â Identify significant features to detect network attacks.4. Â Â The most resistant to adversarial attacks model definition.Various parameters of fully connected neural networks of direct propagation were investigated in this study, such as the number of neurons in hidden layers, activation functions, optimization methods, approaches to training neural networks. The concept of an adversarial attack and an adversarial example, the UNSW-NB 15 network attack database, were also studied.As a result of the work, significant features of the UNSW-NB 15 network attack database were identified. Using these parameters, attempts to conduct adversarial attacks on the created perceptron models were made. Using the metrics definition, the best model was selected.The results can be used to create models of neural networks that must be protected from adversarial attacks.