Distinguishing Tor From Other Encrypted Network Traffic Through Character Analysis
- URL: http://arxiv.org/abs/2405.09412v1
- Date: Wed, 15 May 2024 15:07:31 GMT
- Title: Distinguishing Tor From Other Encrypted Network Traffic Through Character Analysis
- Authors: Pitpimon Choorod, Tobias J. Bauer, Andreas Aßmuth,
- Abstract summary: 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.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: For journalists reporting from a totalitarian regime, whistleblowers and resistance fighters, the anonymous use of cloud services on the Internet can be vital for survival. The Tor network provides a free and widely used anonymization service for everyone. However, there are different approaches to distinguishing Tor from non-Tor encrypted network traffic, most recently only due to the (relative) frequencies of hex digits in a single encrypted payload packet. While conventional data traffic is usually encrypted once, but at least three times in the case of Tor due to the structure and principle of the Tor network, we have examined to what extent the number of encryptions contributes to being able to distinguish Tor from non-Tor encrypted data traffic.
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) - Post Quantum Migration of Tor [0.40964539027092917]
This dissertation proposes an overview of the cryptographic schemes used by Tor.
It highlights the non-quantum-resistant ones and introduces theoretical performance assessment methods of a local Tor network.
arXiv Detail & Related papers (2025-03-13T10:28:03Z) - Revocable Encryption, Programs, and More: The Case of Multi-Copy Security [48.53070281993869]
We show the feasibility of revocable primitives, such as revocable encryption and revocable programs.
This suggests that the stronger notion of multi-copy security is within reach in unclonable cryptography.
arXiv Detail & Related papers (2024-10-17T02:37:40Z) - Polynomial Time Cryptanalytic Extraction of Deep Neural Networks in the Hard-Label Setting [45.68094593114181]
Deep neural networks (DNNs) are valuable assets, yet their public accessibility raises security concerns.
This paper introduces new techniques that, for the first time, achieve cryptanalytic extraction of DNN parameters in the most challenging hard-label setting.
arXiv Detail & Related papers (2024-10-08T07:27:55Z) - 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) - Understanding crypter-as-a-service in a popular underground marketplace [51.328567400947435]
Crypters are pieces of software whose main goal is to transform a target binary so it can avoid detection from Anti Viruses (AVs) applications.
The crypter-as-a-service model has gained popularity, in response to the increased sophistication of detection mechanisms.
This paper provides the first study on an online underground market dedicated to crypter-as-a-service.
arXiv Detail & Related papers (2024-05-20T08:35:39Z) - Quantum Secure Anonymous Communication Networks [2.588445811817417]
We propose a quantum-resistant alternative to RSA and Diffie-Hellman for distributing symmetric keys, namely, quantum key distribution (QKD)
We develop a protocol and network architecture that integrates QKD without the need for trusted nodes, thus meeting the requirements of the Tor network.
arXiv Detail & Related papers (2024-05-09T22:05:45Z) - Feature Analysis of Encrypted Malicious Traffic [3.3148826359547514]
In recent years there has been a dramatic increase in the number of malware attacks that use encrypted HTTP traffic for self-propagation or communication.
Antivirus software and firewalls typically will not have access to encryption keys, and therefore direct detection of encrypted data is unlikely to succeed.
Previous work has shown that traffic analysis can provide indications of malicious intent, even in cases where the underlying data remains encrypted.
arXiv Detail & Related papers (2023-12-06T12:04:28Z) - Revocable Cryptography from Learning with Errors [61.470151825577034]
We build on the no-cloning principle of quantum mechanics and design cryptographic schemes with key-revocation capabilities.
We consider schemes where secret keys are represented as quantum states with the guarantee that, once the secret key is successfully revoked from a user, they no longer have the ability to perform the same functionality as before.
arXiv Detail & Related papers (2023-02-28T18:58:11Z) - Analyzing Trends in Tor [0.5461938536945721]
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.
arXiv Detail & Related papers (2022-08-23T18:31:30Z) - An anomaly detection approach for backdoored neural networks: face
recognition as a case study [77.92020418343022]
We propose a novel backdoored network detection method based on the principle of anomaly detection.
We test our method on a novel dataset of backdoored networks and report detectability results with perfect scores.
arXiv Detail & Related papers (2022-08-22T12:14:13Z) - Machine Learning for Encrypted Malicious Traffic Detection: Approaches,
Datasets and Comparative Study [6.267890584151111]
In post-COVID-19 environment, malicious traffic encryption is growing rapidly.
We formulate a universal framework of machine learning based encrypted malicious traffic detection techniques.
We implement and compare 10 encrypted malicious traffic detection algorithms.
arXiv Detail & Related papers (2022-03-17T14:00:55Z) - Check Your Other Door! Establishing Backdoor Attacks in the Frequency
Domain [80.24811082454367]
We show the advantages of utilizing the frequency domain for establishing undetectable and powerful backdoor attacks.
We also show two possible defences that succeed against frequency-based backdoor attacks and possible ways for the attacker to bypass them.
arXiv Detail & Related papers (2021-09-12T12:44:52Z) - NeuraCrypt: Hiding Private Health Data via Random Neural Networks for
Public Training [64.54200987493573]
We propose NeuraCrypt, a private encoding scheme based on random deep neural networks.
NeuraCrypt encodes raw patient data using a randomly constructed neural network known only to the data-owner.
We show that NeuraCrypt achieves competitive accuracy to non-private baselines on a variety of x-ray tasks.
arXiv Detail & Related papers (2021-06-04T13:42:21Z) - Single-Shot Secure Quantum Network Coding for General Multiple Unicast
Network with Free One-Way Public Communication [56.678354403278206]
We propose a canonical method to derive a secure quantum network code over a multiple unicast quantum network.
Our code correctly transmits quantum states when there is no attack.
It also guarantees the secrecy of the transmitted quantum state even with the existence of an attack.
arXiv Detail & Related papers (2020-03-30T09:25:13Z)
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.