title: ÐÑÑÐ²Ð»ÐµÐ½Ð¸Ðµ Ð´Ð¾ÑÑÑÐ¿Ð° Ðº Ð·Ð°Ð¿ÑÐµÑÐµÐ½Ð½ÑÐ¼ ÑÐ°Ð¹ÑÐ°Ð¼ Ð¿ÑÑÑÐ¼ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° ÑÑÐ°ÑÐ¸ÐºÐ° Tor Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² 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: Identify access to banned sites by analyzing Tor traffic using machine learning methods
Creators: name: ÐÐ¾Ð¶ÑÑÐ¾Ðº, 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°Ð½Ð°Ð»Ð¸Ð· ÑÑÐ°ÑÐ¸ÐºÐ°
Ð¼ÐµÑÐ¾Ð´Ñ ÑÐ¾ÐºÑÐ°ÑÐµÐ½Ð¸Ñ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð² ÑÑÐ°ÑÐ¸ÐºÐ° tor
Ð¼ÐµÑÐ¾Ð´Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ weka
traffic analysis
feature selection of tor traffic
weka classification methods

description: Ð¢ÐµÐ¼Ð° Ð²ÑÐ¿ÑÑÐºÐ½Ð¾Ð¹ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ: "ÐÑÑÐ²Ð»ÐµÐ½Ð¸Ðµ Ð´Ð¾ÑÑÑÐ¿Ð° Ðº Ð·Ð°Ð¿ÑÐµÑÐµÐ½Ð½ÑÐ¼ ÑÐ°Ð¹ÑÐ°Ð¼ Ð¿ÑÑÑÐ¼ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° ÑÑÐ°ÑÐ¸ÐºÐ° Tor Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ". ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° Ð°Ð½Ð°Ð»Ð¸Ð·Ñ Ð¿ÑÐ¸Ð¼ÐµÐ½Ð¸Ð¼Ð¾ÑÑÐ¸ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð´ÐµÐ°Ð½Ð¾Ð½Ð¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ð¸ ÑÑÐ°ÑÐ¸ÐºÐ° ÑÐµÑÐ¸ Tor. 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Ð¾Ð²ÐµÐ´ÐµÐ½Ð¸Ðµ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÑ ÑÐµÑÐ¸ Tor; 2) Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¸Ðµ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° ÑÑÑÐµÑÑÐ²ÑÑÑÐ¸Ñ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ; 3) Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¸Ðµ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð¼ÐµÑÐ¾Ð´Ð¾Ð² ÑÐ¾ÐºÑÐ°ÑÐµÐ½Ð¸Ñ ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ð° Ð¼ÐµÑÑÐ¸Ðº ÑÐµÑÐµÐ²ÑÑ Ð¿Ð°ÐºÐµÑÐ¾Ð²; 4) Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¸Ðµ ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ¾Ð² Ñ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÐ¼Ð¸ Ð¼ÐµÑÐ¾Ð´Ð°Ð¼Ð¸ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ; 5) Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¸Ðµ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° ÐºÐ°ÑÐµÑÑÐ²Ð° Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ ÑÐµÑÐµÐ²ÑÑ Ð¿Ð°ÐºÐµÑÐ¾Ð², ÑÐ¾Ð´ÐµÑÐ¶Ð°ÑÐ¸Ñ Ð·Ð°Ð¿ÑÐ¾ÑÑ Ðº Ð·Ð°Ð¿ÑÐµÑÐµÐ½Ð½ÑÐ¼ ÑÐ°Ð¹ÑÐ°Ð¼. Ð Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ Ð±ÑÐ»Ð° ÑÐ¾Ð±ÑÐ°Ð½Ð° Ð²ÑÐ±Ð¾ÑÐºÐ° Ñ ÑÑÐ°ÑÐ¸ÐºÐ¾Ð¼ Ðº Ð·Ð°Ð¿ÑÐµÑÐµÐ½Ð½ÑÐ¼ Ð¸ Ð»ÐµÐ³Ð¸ÑÐ¸Ð¼Ð½ÑÐ¼ ÑÐ°Ð¹ÑÐ°Ð¼. ÐÐ»Ñ Ð¿Ð¾Ð»ÑÑÐµÐ½Ð¸Ñ Ð¼ÐµÑÑÐ¸Ðº ÑÐ°Ð¹ÑÐ° Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð»ÑÑ Ð¸Ð½ÑÑÑÑÐ¼ÐµÐ½Ñ CICFlowMeter, Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÑÑÐ¸Ð¹ Ð¿Ð¾Ð»ÑÑÐ¸ÑÑ Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÐµ ÑÐ°ÑÐ°ÐºÑÐµÑÐ¸ÑÑÐ¸ÐºÐ¸. ÐÐ»Ñ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¸Ñ ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ¾Ð² Ñ Ð¼ÐµÑÐ¾Ð´Ð°Ð¼Ð¸ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¸ Ð¼ÐµÑÐ¾Ð´Ð°Ð¼Ð¸ ÑÐ¾ÐºÑÐ°ÑÐµÐ½Ð¸Ñ ÑÐ°Ð·Ð¼ÐµÑÐ½Ð¾ÑÑÐ¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð»ÑÑ Ð¸Ð½ÑÑÑÑÐ¼ÐµÐ½Ñ Weka. Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ Ð¼ÐµÑÐ¾Ð´Ð¾Ð¼ RandomForest Ð² ÐºÐ¾Ð¼Ð±Ð¸Ð½Ð°ÑÐ¸Ð¸ Ñ WrapperSubsetEval Ð´Ð¾ÑÑÐ¸Ð³Ð°ÐµÑÑÑ Ð¼Ð°ÐºÑÐ¸Ð¼Ð°Ð»ÑÐ½Ð°Ñ ÑÐ¾ÑÐ½Ð¾ÑÑÑ Ð² 98%. Ð¢Ð°Ðº Ð¶Ðµ Ð±ÑÐ»Ð¸ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ñ ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÑ Ð¿Ð¾ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ñ ÐºÐ¾Ð½ÐºÑÐµÑÐ½Ð¾Ð³Ð¾ ÑÐ°Ð¹ÑÐ°. ÐÐ°Ð¸Ð»ÑÑÑÐ¸Ð¹ ÑÐµÐ·ÑÐ»ÑÑÐ°Ñ Ñ ÑÐ¾ÑÐ½Ð¾ÑÑÑÑ Ð² 69% Ð±ÑÐ» Ð´Ð¾ÑÑÐ¸Ð³Ð½ÑÑ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° RandomForest. ÐÐ°Ð½Ð½ÑÐµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¼Ð¾Ð³ÑÑ Ð±ÑÑÑ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ñ Ð² ÑÐ¸ÑÑÐµÐ¼Ð°Ñ, ÑÐ¸Ð»ÑÑÑÑÑÑÐ¸Ñ ÑÑÐ°ÑÐ¸Ðº Ð½Ð° ÑÑÐ¾ÑÐ¾Ð½Ðµ Ð¿ÑÐ¾Ð²Ð°Ð¹Ð´ÐµÑÐ°.
description: The subject of the graduate qualification work is âIdentify access to banned sites by analyzing Tor traffic using machine learning methodsâ. The given work is devoted to the analysis of the applicability of machine learning methods to deanonymize Tor network traffic. The research set the following goals: 1) analyzing the architecture of the Tor network; 2) analyzing existing machine learning methods; 3) analyzing methods for selecting features network packets; 4) conducting experiments with various machine learning methods; 5) analyzing the quality of detection of network packets containing requests to banned sites. In this work, a sample was collected with traffic to banned and legitimate sites. To get site metrics, we used the CICFlowMeter tool, which allows us to get temporary characteristics. The Weka tool was used to conduct experiments with machine learning methods and selection features methods. As a result, the RandomForest method in combination with WrapperSubsetEval achieves a maximum accuracy of 98%. Experiments were also conducted to determine a specific site. The best result with 69% accuracy was achieved using the RandomForest algorithm. These results can be applied in systems that filter traffic on the provider side.