title: ÐÐ±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ðµ Ð±Ð¾ÑÐ½ÐµÑÐ¾Ð² Ð² ÑÐµÑÑÑ ÐÐ½ÑÐµÑÐ½ÐµÑÐ° Ð²ÐµÑÐµÐ¹ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸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: Detecting botnets in Internet of things networks based on machine learning
Creators: name: Ð§ÐµÑÐ½ÑÐ²ÑÐºÐ°Ñ, ÐÐ½Ð½Ð°-ÐÑÐ°Ð¶Ð¸Ð½Ð°
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: 2020
Subjects: ÐÐ°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐÐ½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¾Ð½Ð½ÑÐµ ÑÐ¸ÑÑÐµÐ¼Ñ
ÐÐ½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ñ
Ð¸Ð½ÑÐµÑÐ½ÐµÑ Ð²ÐµÑÐµÐ¹
Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ðµ Ð±Ð¾ÑÐ½ÐµÑÐ¾Ð²
ÑÐµÑÐµÐ²Ð°Ñ Ð±ÐµÐ·Ð¾Ð¿Ð°ÑÐ½Ð¾ÑÑÑ
internet of things
botnet detection
network security

description: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° Ð¾Ð±ÐµÑÐ¿ÐµÑÐµÐ½Ð¸Ñ Ð·Ð°ÑÐ¸ÑÑ ÑÐ¸ÑÑÐµÐ¼ ÐÐ½ÑÐµÑÐ½ÐµÑÐ° Ð²ÐµÑÐµÐ¹ Ð¾Ñ Ð°ÑÐ°Ðº, ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ð½ÑÑ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð±Ð¾ÑÐ½ÐµÑÐ¾Ð², Ð¿ÑÑÐµÐ¼ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ, ÑÑÐ¸ÑÑÐ²Ð°ÑÑÐ¸Ñ ÑÐ¿ÐµÑÐ¸ÑÐ¸ÐºÑ Ð¿Ð¾Ð´Ð¾Ð±Ð½ÑÑ ÑÐ¸ÑÑÐµÐ¼. 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: 1. ÐÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ ÑÐ¿ÐµÑÐ¸ÑÐ¸ÐºÐ¸ Ð±Ð¾ÑÐ½ÐµÑÐ¾Ð² Ð² ÑÐµÑÑÑ ÐÐ½ÑÐµÑÐ½ÐµÑÐ° Ð²ÐµÑÐµÐ¹. 2. ÐÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¼ÐµÑÐ¾Ð´Ð¾Ð², Ð¿ÑÐ¸Ð¼ÐµÐ½ÑÐµÐ¼ÑÑ Ð´Ð»Ñ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð±Ð¾ÑÐ½ÐµÑÐ¾Ð² Ð¸ Ð²ÑÐ´ÐµÐ»ÐµÐ½Ð¸Ðµ Ð½Ð°Ð¸Ð±Ð¾Ð»ÐµÐµ Ð¿ÐµÑÑÐ¿ÐµÐºÑÐ¸Ð²Ð½ÑÑ. 3. Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¿Ð¾Ð´ÑÐ¾Ð´Ð° Ðº Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð±Ð¾ÑÐ½ÐµÑÐ¾Ð² Ð² ÑÐµÑÑÑ ÐÐ½ÑÐµÑÐ½ÐµÑÐ° Ð²ÐµÑÐµÐ¹ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. 4. Ð ÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð½Ð¾Ð³Ð¾ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð° Ð¸ ÑÑÐºÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ°Ð»ÑÐ½Ð°Ñ Ð¾ÑÐµÐ½ÐºÐ° ÐµÐ³Ð¾ ÐºÐ°ÑÐµÑÑÐ²Ð° Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð±Ð¾ÑÐ½ÐµÑÐ¾Ð². Ð Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ Ð±ÑÐ»Ð¸ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ñ Ð¸ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ñ ÑÑÐ¸ Ð¼Ð¾Ð´ÐµÐ»Ð¸, Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð½ÑÐµ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ ÑÐ»ÐµÐ´ÑÑÑÐ¸Ñ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ: Ð°Ð²ÑÐ¾ÑÐ½ÐºÐ¾Ð´ÐµÑ, Ð¼Ð½Ð¾Ð³Ð¾ÑÐ»Ð¾Ð¹Ð½ÑÐ¹ Ð¿ÐµÑÑÐµÐ¿ÑÑÐ¾Ð½ (MLP) Ð¸ Ð¼ÐµÑÐ¾Ð´ K Ð±Ð»Ð¸Ð¶Ð°Ð¹ÑÐ¸Ñ ÑÐ¾ÑÐµÐ´ÐµÐ¹ (K-NN). ÐÐ»Ñ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¸Ñ ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ¾Ð² Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð»ÑÑ Ð½Ð°Ð±Ð¾Ñ Ð´Ð°Ð½Ð½ÑÑ Bot-IoT, ÑÐ¾Ð´ÐµÑÐ¶Ð°ÑÐ¸Ð¹ Ð² ÑÐµÐ±Ðµ ÑÐ¾ÑÐµÑÐ°Ð½Ð¸Ðµ Ð¾Ð±ÑÑÐ½Ð¾Ð³Ð¾ ÑÑÐ°ÑÐ¸ÐºÐ° Ð¸ ÑÑÐ°ÑÐ¸ÐºÐ° Ð±Ð¾ÑÐ½ÐµÑÐ¾Ð². Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ, Ð¼Ð¾Ð´ÐµÐ»Ñ, Ð² ÐºÐ¾ÑÐ¾ÑÐ¾Ð¹ Ð±ÑÐ»Ð¸ ÑÐºÐ¾Ð¼Ð±Ð¸Ð½Ð¸ÑÐ¾Ð²Ð°Ð½Ñ Ð´Ð²Ð° Ð¼ÐµÑÐ¾Ð´Ð° Ð°Ð²ÑÐ¾ÑÐ½ÐºÐ¾Ð´ÐµÑ Ð¸ K-NN, Ð¿Ð¾ÐºÐ°Ð·ÑÐ²Ð°ÐµÑ Ð»ÑÑÑÐ¸Ðµ Ð·Ð½Ð°ÑÐµÐ½Ð¸Ñ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ Ð¸ F-Ð¼ÐµÑÑ 99,72% Ð¸ 99,3% ÑÐ¾Ð¾ÑÐ²ÐµÑÑÑÐ²ÐµÐ½Ð½Ð¾. 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¿ÑÐ¸ Ð°Ð½Ð°Ð»Ð¸Ð·Ðµ Ð±ÐµÐ·Ð¾Ð¿Ð°ÑÐ½Ð¾ÑÑÐ¸ ÑÐµÑÐµÐ²Ð¾Ð¹ Ð¸Ð½ÑÑÐ°ÑÑÑÑÐºÑÑÑÑ ÑÐ¸ÑÑÐµÐ¼ ÐÐ½ÑÐµÑÐ½ÐµÑÐ° Ð²ÐµÑÐµÐ¹, Ð° ÑÐ°ÐºÐ¶Ðµ ÐºÑÑÐ¿Ð½Ð¾Ð¼Ð°ÑÑÑÐ°Ð±Ð½ÑÑ ÑÐ¸ÑÑÐµÐ¼ Ð² ÑÐ¾ÑÑÐ°Ð² ÐºÐ¾ÑÐ¾ÑÑÑ Ð²ÑÐ¾Ð´ÑÑ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑÑÐ°Ð»ÑÐ½ÑÐµ ÑÑÑÑÐ¾Ð¹ÑÑÐ²Ð°.
description: This work is devoted to the protection of the Internet of things systems from attacks realized with the help of botnets by using machine learning methods that take into account the specifics of such systems. Tasks that were solved during the study: 1. The study of the specifics of botnets in the networks of the Internet of things. 2. The study of methods used to detect botnets and highlight the most promising. 3. Development of an approach to detecting botnets on the Internet of things using machine learning. 4. Implementation of the proposed approach and experimental assessment of its botnet detection quality. In this work, three models were constructed and studied, based on the following machine learning methods: auto-encoder, multilayer perceptron (MLP) and the method of K nearest neighbors (K-NN). For the experiments the Bot-IoT dataset was used, containing a combination of regular traffic and botnet traffic. As a result, the model in which the two auto-encoder and K-NN methods were combined shows the best accuracy and F-measures of 99.72% and 99.3%, respectively. The results can be used in analyzing the security of the network infrastructure of the Internet of things, as well as large-scale systems that include intelligent devices.