title: Ð¡ÑÐ°Ð²Ð½ÐµÐ½Ð¸Ðµ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð²ÑÑÐ²Ð»ÐµÐ½Ð¸Ñ Ð°ÑÐ°Ðº Ð² ÑÑÐ°Ð½ÑÐ¿Ð¾ÑÑÐ½ÑÑ ÑÐµÑÑ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: Comparison of machine learning methods to detect attacks in transport networks
Creators: name: ÐÐ°Ð»ÑÑÐµÐ², ÐÐ¾Ð½ÑÑÐ°Ð½ÑÐ¸Ð½
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ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÐÐ°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐÐ½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ñ
Ð±ÐµÑÐ¿ÑÐ¾Ð²Ð¾Ð´Ð½ÑÐµ ÑÑÐ°Ð½ÑÐ¿Ð¾ÑÑÐ½ÑÐµ ÑÐµÑÐ¸
ÑÐµÑÐµÐ²Ð°Ñ Ð±ÐµÐ·Ð¾Ð¿Ð°ÑÐ½Ð¾ÑÑÑ
Ð²ÑÑÐ²Ð»ÐµÐ½Ð¸Ðµ Ð°ÑÐ°Ðº
Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ðµ Ð°Ð½Ð¾Ð¼Ð°Ð»Ð¸Ð¹
vanet
network security
attack detection
anomaly detection

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description: The given work is devoted to studying of the security of wireless transport networks. As a solution to the problem of detecting attacks, a prototype intrusion detection system was developed based on five machine learning methods, among which three are representatives of the class of classical training in classification problems (Random Forest, Support Vector Machine and Naive Bayesian Classifier), and the other two are representatives class of neural networks (perceptron and neural network with long short-term memory). As attacks were chosen those that are aimed at violating the routing of transport networks, as well as the classic Denial of Service attack, inherent in all types of networks. To generate traffic, a NS-3 network simulator based on a reactive protocol was used. Parameters were selected to identify each attack. An analysis of the selected parameters was also carried out to determine their effect on a particular attack. The developed prototype for detecting attacks in transport networks was trained and tested. An evaluation of the effectiveness of the prototype was also carried out using various metrics. The effectiveness of the selected machine learning methods for identifying attacks based on various metrics is compared, and a conclusion is made regarding the applicability of machine learning methods to the problem under consideration.