Abstract

Privacy enhancing technologies (PETs) are ubiquitous nowadays. They are beneficial for a wide range of users: for businesses, journalists, bloggers, etc. However, PETs are not always used for legal activity. There a lot of anonymous networks and technologies which grants anonymous access to digital resources. The most popular anonymous networks nowadays is Tor. Tor is a valuable tool for hackers, drug and gun dealers. The present paper is focused on Tor users’ deanonimization using out-of-the box technologies and a basic machine learning algorithm. The aim of the work is to show that it is possible to deanonimize a small fraction of users without having a lot of resources and state-of-the-art machine learning techniques. The first stage of the research was the investigation of contemporary anonymous networks. The second stage was the investigation of deanonimization techniques: traffic analysis, timing attacks, attacks with autonomous systems. For our system, we used website fingerprinting attack, because it requires the smallest number of resources needed for successful implementation of the attack. Finally, there was an experiment held with 5 persons in one room with one corrupted entry Tor relay. We achieved a quite good accuracy (70%) for classifying the webpage, which the user visits, using the set of resources provided by global cybersecurity company. The deanonimization is a very important task from the point of view of national security.

Highlights

  • Internet privacy is considered as an integral part of freedom of speech

  • The Deep Web is a layer of the Internet, which can not be accessed by traditional search engines, so the content in this layer is not indexed

  • This attack has been widely discussed in the researcherscommunity because it has proven its effectiveness against various privacy enhancing technologies, such as Tor, SSL and VPN

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Summary

Introduction

A lot of people are concerned about their anonymity in public and there is a growing need for privacy enhancing technologies. The Deep Web is a layer of the Internet, which can not be accessed by traditional search engines, so the content in this layer is not indexed. There are a lot of networks and technologies, which grant access to deep web resources, for example, Tor, I2P, Freenet, etc. Each of these instruments hides users’ traffic from adversaries, making the deanonimization a hard thing to do. Our research focuses on Tor users’ deanonimization, because of its popularity and prevalence

Tor background
Deanonimization techniques
Website Fingerprinting Attack Overview
Big number of various Packet spinning attack corrupted nodes
The Oracle Problem
Real World Scenario
Deanonimization system scheme
Data Mining
Feature Extraction
Machine Learning Module
Experimental Environment
Data Gathering
Machine Learning Model
Evalution metrics
Experimental results
Conclusion
Future work

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