Analyzing Trends in Tor

• URL: http://arxiv.org/abs/2208.11149v3
• Date: Mon, 9 Sep 2024 00:44:44 GMT
• Title: Analyzing Trends in Tor
• Authors: Chaitanya Rahalkar, Anushka Virgaonkar, Kethaki Varadan,
• Abstract summary: Tor was originally started in the Naval Research Laboratory for anonymous Internet browsing and Internet-based communication. From being used for anonymous communications, it has now segmented into various other use-cases like censorship circumvention, performing illegal activities, etc.
• Score: 0.5461938536945721
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Tor Network has been a significant part of the Internet for years. Tor was originally started in the Naval Research Laboratory for anonymous Internet browsing and Internet-based communication. From being used for anonymous communications, it has now segmented into various other use-cases like censorship circumvention, performing illegal activities, etc. In this paper, we perform empirical measurements on the Tor network to analyze the trends in Tor over the years. We gather our measurements data through our measurement scripts, past research in this domain, and aggregated data provided by the Tor metrics directory. We use this data to analyze trends and understand the incidents that caused fluctuations in the trends of different data parameters. We collect measurements data for Tor parameters like Tor users, onion services, Tor relays, and bridges, etc. We also study censorshiprelated events and study trends by analyzing censorship-related metrics. Finally, we touch upon the location diversity in Tor and study how the Tor circuit selection and construction are impacted by the bandwidth distribution of Tor relays across geographies.

Related papers

• Snorkeling in dark waters: A longitudinal surface exploration of unique Tor Hidden Services (Extended Version) [2.498836880652668]
  The Onion Router (Tor) is a controversial network whose utility is constantly under scrutiny. In this work, we present a large-scale analysis of the Tor Network. We leverage our crawler, dubbed Mimir, which automatically collects and visits content linked within the pages to collect a dataset of pages from more than 25k sites.
  arXiv  Detail & Related papers  (2025-04-23T15:59:16Z)
• Model Inversion Attacks: A Survey of Approaches and Countermeasures [59.986922963781]
  Recently, a new type of privacy attack, the model inversion attacks (MIAs), aims to extract sensitive features of private data for training. Despite the significance, there is a lack of systematic studies that provide a comprehensive overview and deeper insights into MIAs. This survey aims to summarize up-to-date MIA methods in both attacks and defenses.
  arXiv  Detail & Related papers  (2024-11-15T08:09:28Z)
• Progressive Pruning: Analyzing the Impact of Intersection Attacks [1.8434042562191815]
  Stream-based communication poses unique challenges for anonymous communication networks (ACNs) Traditionally designed for independent messages, ACNs struggle to account for the inherent vulnerabilities of streams. We introduce progressive pruning, a novel methodology for quantifying the susceptibility to intersection attacks.
  arXiv  Detail & Related papers  (2024-10-11T10:40:51Z)
• Unveiling the Digital Fingerprints: Analysis of Internet attacks based on website fingerprints [0.0]
  We show that using the newest machine learning algorithms an attacker can deanonymize Tor traffic by applying such techniques. We capture network packets across 11 days, while users navigate specific web pages, recording data in.pcapng format through the Wireshark network capture tool.
  arXiv  Detail & Related papers  (2024-09-01T18:44:40Z)
• ParTEETor: A System for Partial Deployments of TEEs within Tor [6.668496876628325]
  ParTEETor is a Tor-variant system that uses partial deployments of trusted execution environments to thwart known attacks. We evaluate ParTEETor for security, performance, and privacy.
  arXiv  Detail & Related papers  (2024-08-26T21:23:19Z)
• Differentially Private Data Release on Graphs: Inefficiencies and Unfairness [48.96399034594329]
  This paper characterizes the impact of Differential Privacy on bias and unfairness in the context of releasing information about networks. We consider a network release problem where the network structure is known to all, but the weights on edges must be released privately. Our work provides theoretical foundations and empirical evidence into the bias and unfairness arising due to privacy in these networked decision problems.
  arXiv  Detail & Related papers  (2024-08-08T08:37:37Z)
• Distinguishing Tor From Other Encrypted Network Traffic Through Character Analysis [0.0]
  The Tor network provides a free and widely used anonymization service for everyone. There are different approaches to distinguishing Tor from non-Tor encrypted network traffic. We have examined to what extent the number of encryptions contributes to being able to distinguish Tor from non-Tor encrypted data traffic.
  arXiv  Detail & Related papers  (2024-05-15T15:07:31Z)
• A Measurement of Genuine Tor Traces for Realistic Website Fingerprinting [5.4482836906033585]
  Website fingerprinting (WF) is a dangerous attack on web privacy because it enables an adversary to predict the website a user is visiting. We present GTT23, the first WF dataset of genuine Tor traces, which we obtain through a large-scale measurement of the Tor network.
  arXiv  Detail & Related papers  (2024-04-11T16:24:49Z)
• Identification of Twitter Bots based on an Explainable ML Framework: the US 2020 Elections Case Study [72.61531092316092]
  This paper focuses on the design of a novel system for identifying Twitter bots based on labeled Twitter data. Supervised machine learning (ML) framework is adopted using an Extreme Gradient Boosting (XGBoost) algorithm. Our study also deploys Shapley Additive Explanations (SHAP) for explaining the ML model predictions.
  arXiv  Detail & Related papers  (2021-12-08T14:12:24Z)
• Measuring the impact of spammers on e-mail and Twitter networks [0.0]
  This paper investigates if senders of large amounts of irrelevant or unsolicited information - commonly called "spammers" - distort the network structure of social networks. Two large social networks are analyzed, the first extracted from the Twitter discourse about a big telecommunication company, and the second obtained from three years of email communication of 200 managers working for a large multinational company. Results show that spammers do not significantly alter the structure of the information-carrying network, for most of the social indicators.
  arXiv  Detail & Related papers  (2021-05-21T10:13:11Z)
• Constructing interval variables via faceted Rasch measurement and multitask deep learning: a hate speech application [63.10266319378212]
  We propose a method for measuring complex variables on a continuous, interval spectrum by combining supervised deep learning with the Constructing Measures approach to faceted Rasch item response theory (IRT) We demonstrate this new method on a dataset of 50,000 social media comments sourced from YouTube, Twitter, and Reddit and labeled by 11,000 U.S.-based Amazon Mechanical Turk workers.
  arXiv  Detail & Related papers  (2020-09-22T02:15:05Z)
• Backdoor Learning: A Survey [75.59571756777342]
  Backdoor attack intends to embed hidden backdoor into deep neural networks (DNNs) Backdoor learning is an emerging and rapidly growing research area. This paper presents the first comprehensive survey of this realm.
  arXiv  Detail & Related papers  (2020-07-17T04:09:20Z)
• Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Process [63.75363908696257]
  We review the methods that have been applied to network data with the purpose of developing an intrusion detector. We discuss the techniques used for the capture, preparation and transformation of the data, as well as, the data mining and evaluation methods. As a result of this literature review, we investigate some open issues which will need to be considered for further research in the area of network security.
  arXiv  Detail & Related papers  (2020-01-27T11:21:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.