title: ÐÐ´Ð°Ð¿ÑÐ°ÑÐ¸Ñ Ð¿ÐµÑÑÐµÐ¿ÑÑÐ¾Ð½Ð½Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ ÑÐµÑÐµÐ²ÑÑ Ð°ÑÐ°Ðº Ð´Ð»Ñ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ Ð¸ÑÑ 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: Adaptation of the perceptron model for detecting network attacks for various initial data
Creators: name: ÐÑÐ»ÐµÐµÐ²Ð°, ÐÐ½Ð½Ð°
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: 2024
Subjects: ÐÐ°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐÐ½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ñ
Ð¿ÐµÑÑÐµÐ¿ÑÑÐ¾Ð½
Ð°ÑÐ°ÐºÐ¸ Ð½Ð° Ð¸Ð½ÑÐµÑÐ½ÐµÑ Ð²ÐµÑÐµÐ¹
ÑÐµÑÐµÐ²Ð¾Ð¹ ÑÑÐµÐº
Ð¿Ð¾Ð¸ÑÐº ÑÑÐ·Ð²Ð¸Ð¼Ð¾ÑÑÐµÐ¹
perceptron
internet of things attacks
network stack
vulnerability search

description: Ð¦ÐµÐ»ÑÑ ÑÐ°Ð±Ð¾ÑÑ ÑÐ²Ð»ÑÐµÑÑÑ Ð°Ð´Ð°Ð¿ÑÐ°ÑÐ¸Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð°ÑÐ°Ðº Ð´Ð»Ñ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ Ð½Ð°Ð±Ð¾ÑÐ¾Ð² Ð´Ð°Ð½Ð½ÑÑ. ÐÑÐµÐ´Ð¼ÐµÑÐ¾Ð¼ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÐ²Ð»ÑÐµÑÑÑ Ð°Ð´Ð°Ð¿ÑÐ°ÑÐ¸Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. ÐÐ°Ð´Ð°ÑÐ¸, ÑÐµÑÐ°ÐµÐ¼ÑÐµ Ð² ÑÐ¾Ð´Ðµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ: 1. ÐÑÐ¾Ð°Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°ÑÑ Ð½Ð°Ð±Ð¾ÑÑ Ð´Ð°Ð½Ð½ÑÑ, Ð²ÑÐ±ÑÐ°ÑÑ Ð½Ð°Ð¸Ð±Ð¾Ð»ÐµÐµ Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð²Ð½ÑÐµ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¸ Ð´Ð»Ñ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸. 2. Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐ°ÑÑ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½ÑÐ¹ Ð¿ÑÐ¾ÑÐ¾ÑÐ¸Ð¿ Ð¿ÐµÑÑÐµÐ¿ÑÑÐ¾Ð½Ð°, Ð¾Ð¿ÑÐµÐ´ÐµÐ»Ð¸ÑÑ Ð³Ð¸Ð¿ÐµÑÐ¿Ð°ÑÐ°Ð¼ÐµÑÑÑ ÐÐ¡, Ð²Ð»Ð¸ÑÑÑÐ¸Ðµ Ð½Ð° ÐºÐ°ÑÐµÑÑÐ²Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. 3. ÐÐ´Ð°Ð¿ÑÐ¸ÑÐ¾Ð²Ð°ÑÑ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð´Ð»Ñ Ð¿Ð¾Ð»ÑÑÐµÐ½Ð¸Ñ Ð²ÑÑÐ¾ÐºÐ¾Ð³Ð¾ ÐºÐ°ÑÐµÑÑÐ²Ð° Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. 4. ÐÑÐ¾Ð°Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°ÑÑ Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½ÑÐµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ. Ð ÑÐ¾Ð´Ðµ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð° Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð° Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÐ° Ð¿ÐµÑÑÐµÐ¿ÑÑÐ¾Ð½Ð° Ð¸ ÐµÐ³Ð¾ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð². ÐÑÐ±ÑÐ°Ð½Ð½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð´Ð¾Ð»Ð¶Ð½Ð° ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÑÐ¸ÑÐ¾Ð²Ð°ÑÑ Ð°ÑÐ°ÐºÐ¸ Ð² ÑÐµÑÐ¸ Ð¸Ð½ÑÐµÑÐ½ÐµÑÐ° Ð²ÐµÑÐµÐ¹. ÐÑÐ»Ð¸ Ð¿ÑÐ¾Ð°Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ñ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¿ÑÐ¸ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ°Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸. Ð ÑÐ°ÑÑÐ½Ð¾ÑÑÐ¸, Ð¿ÑÐ¾Ð²Ð¾Ð´Ð¸Ð»Ð¸ÑÑ ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÑ Ð¿ÑÐ¸ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¾ÑÐ°Ñ, ÑÑÐ½ÐºÑÐ¸ÑÑ Ð°ÐºÑÐ¸Ð²Ð°ÑÐ¸Ð¸, ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ðµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð¾Ð², Ð° ÑÐ°ÐºÐ¶Ðµ Ð¿ÑÐ¸ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ Ð¼ÐµÑÐ¾Ð´Ð°Ñ ÑÑÐ°Ð½Ð´Ð°ÑÑÐ¸Ð·Ð°ÑÐ¸Ð¸ Ð¸ Ð¼Ð°ÑÑÑÐ°Ð±Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð´Ð°Ð½Ð½ÑÑ Ð¿ÐµÑÐµÐ´ Ð½ÐµÐ¿Ð¾ÑÑÐµÐ´ÑÑÐ²ÐµÐ½Ð½ÑÐ¼ Ð¾Ð±ÑÑÐµÐ½Ð¸ÐµÐ¼. Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð° ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð° Ð¼Ð¾Ð´ÐµÐ»Ñ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ, ÐºÐ¾ÑÐ¾ÑÐ°Ñ Ð¿Ð¾ÐºÐ°Ð·ÑÐ²Ð°ÐµÑ ÐºÐ°ÑÐµÑÑÐ²Ð¾ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ ÑÐ²ÑÑÐµ 90% Ð½Ð° ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ Ð½Ð°Ð±Ð¾ÑÐ°Ñ Ð´Ð°Ð½Ð½ÑÑ, ÑÐ¾Ð´ÐµÑÐ¶Ð°ÑÐ¸Ñ ÑÑÐ°ÑÐ¸Ðº ÑÐµÑÐ¸ ÑÑÑÑÐ¾Ð¹ÑÑÐ² Ð¸Ð½ÑÐµÑÐ½ÐµÑÐ° Ð²ÐµÑÐµÐ¹, Ð° Ð¸Ð¼ÐµÐ½Ð½Ð¾ NF-UQ-NIDS-v2, CIC IoT 2023, CIC IoMT 2024.
description: The purpose of the study is to adapt the attack detection model for various datasets. The subject of the research is the adaptation of the machine learning model. The research set the following goals: 1. Analyze datasets and select the most informative features for training the neural network. 2. Develop a software prototype of the perceptron, and determine the neural network hyperparameters that affect the quality of training. 3. Adapt the model to achieve high training quality. 4. Analyze the obtained results. During the work the architecture of the perceptron and its parameters were studied. The chosen model should classify attacks in the Internet of Things network. The results were analyzed with various parameters of the neural network. In particular, experiments were conducted with different optimizers, activation functions, the number of neurons, as well as with various methods of data standardization and scaling before training. As a result of the work, a machine learning model was developed that shows a classification quality above 90% on various datasets containing traffic from Internet of Things devices, namely NF-UQ-NIDS-v2, CIC IoT 2023, and CIC IoMT 2024.