Unveiling the Digital Fingerprints: Analysis of Internet attacks based on website fingerprints

• URL: http://arxiv.org/abs/2409.03791v1
• Date: Sun, 1 Sep 2024 18:44:40 GMT
• Title: Unveiling the Digital Fingerprints: Analysis of Internet attacks based on website fingerprints
• Authors: Blerim Rexha, Arbena Musa, Kamer Vishi, Edlira Martiri,
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Parallel to our physical activities our virtual presence also leaves behind our unique digital fingerprints, while navigating on the Internet. These digital fingerprints have the potential to unveil users' activities encompassing browsing history, utilized applications, and even devices employed during these engagements. Many Internet users tend to use web browsers that provide the highest privacy protection and anonymization such as Tor. The success of such privacy protection depends on the Tor feature to anonymize end-user IP addresses and other metadata that constructs the website fingerprint. In this paper, we show that using the newest machine learning algorithms an attacker can deanonymize Tor traffic by applying such techniques. In our experimental framework, we establish a baseline and comparative reference point using a publicly available dataset from Universidad Del Cauca, Colombia. We capture network packets across 11 days, while users navigate specific web pages, recording data in .pcapng format through the Wireshark network capture tool. Excluding extraneous packets, we employ various machine learning algorithms in our analysis. The results show that the Gradient Boosting Machine algorithm delivers the best outcomes in binary classification, achieving an accuracy of 0.8363. In the realm of multi-class classification, the Random Forest algorithm attains an accuracy of 0.6297.

Related papers

• Fingerprinting and Tracing Shadows: The Development and Impact of Browser Fingerprinting on Digital Privacy [55.2480439325792]
  Browser fingerprinting is a growing technique for identifying and tracking users online without traditional methods like cookies. This paper gives an overview by examining the various fingerprinting techniques and analyzes the entropy and uniqueness of the collected data.
  arXiv  Detail & Related papers  (2024-11-18T20:32:31Z)
• How Unique is Whose Web Browser? The role of demographics in browser fingerprinting among US users [50.699390248359265]
  Browser fingerprinting can be used to identify and track users across the Web, even without cookies. This technique and resulting privacy risks have been studied for over a decade. We provide a first-of-its-kind dataset to enable further research.
  arXiv  Detail & Related papers  (2024-10-09T14:51:58Z)
• Assessing Web Fingerprinting Risk [2.144574168644798]
  Browser fingerprints are device-specific identifiers that enable covert tracking of users even when cookies are disabled. Previous research has established entropy, a measure of information, as the key metric for quantifying fingerprinting risk. We provide the first study of browser fingerprinting which addresses the limitations of prior work.
  arXiv  Detail & Related papers  (2024-03-22T20:34:41Z)
• The Key to Deobfuscation is Pattern of Life, not Overcoming Encryption [0.7124736158080939]
  We present a novel methodology that is effective at deobfuscating sources by synthesizing measurements from key locations along protocol transaction paths. Our approach links online personas with their origin IP addresses based on a Pattern of Life (PoL) analysis. We show that, when monitoring in the correct places on the Internet, DNS over HTTPS (DoH) and DNS over TLS (DoT) can be deobfuscated with up to 100% accuracy.
  arXiv  Detail & Related papers  (2023-10-04T02:34:29Z)
• Your Room is not Private: Gradient Inversion Attack on Reinforcement Learning [47.96266341738642]
  Privacy emerges as a pivotal concern within the realm of embodied AI, as the robot accesses substantial personal information. This paper proposes an attack on the value-based algorithm and the gradient-based algorithm, utilizing gradient inversion to reconstruct states, actions, and supervision signals.
  arXiv  Detail & Related papers  (2023-06-15T16:53:26Z)
• Efficient and Low Overhead Website Fingerprinting Attacks and Defenses based on TCP/IP Traffic [16.6602652644935]
  Website fingerprinting attacks based on machine learning and deep learning tend to use the most typical features to achieve a satisfactory performance of attacking rate. To defend against such attacks, random packet defense (RPD) with a high cost of excessive network overhead is usually applied. We propose a filter-assisted attack against RPD, which can filter out the injected noises using the statistical characteristics of TCP/IP traffic. We further improve the list-based defense by a traffic splitting mechanism, which can combat the mentioned attacks as well as save a considerable amount of network overhead.
  arXiv  Detail & Related papers  (2023-02-27T13:45:15Z)
• 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)
• Uncovering Fingerprinting Networks. An Analysis of In-Browser Tracking using a Behavior-based Approach [0.0]
  This thesis explores the current state of browser fingerprinting on the internet. We implement FPNET to identify fingerprinting scripts on large sets of websites by observing their behavior. We track down companies like Google, Yandex, Maxmind, Sift, or FingerprintJS.
  arXiv  Detail & Related papers  (2022-08-15T18:06:25Z)
• Predicting Process Name from Network Data [0.0]
  We report on a machine learning technique capable of using netflow-like features to predict the application that generated the traffic. In our experiments, we used ground-truth labels obtained from host-based sensors deployed in a large enterprise environment. We demonstrate how machine learning models can achieve high classification accuracy using only netflow-like features as the basis for classification.
  arXiv  Detail & Related papers  (2021-09-03T20:15:34Z)
• MixNet for Generalized Face Presentation Attack Detection [63.35297510471997]
  We have proposed a deep learning-based network termed as textitMixNet to detect presentation attacks. The proposed algorithm utilizes state-of-the-art convolutional neural network architectures and learns the feature mapping for each attack category.
  arXiv  Detail & Related papers  (2020-10-25T23:01:13Z)
• Keystroke Biometrics in Response to Fake News Propagation in a Global Pandemic [77.79066811371978]
  This work proposes and analyzes the use of keystroke biometrics for content de-anonymization. Fake news have become a powerful tool to manipulate public opinion, especially during major events.
  arXiv  Detail & Related papers  (2020-05-15T17:56:11Z)

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.