Performance analysis of common browser extensions for cryptojacking detection

• URL: http://arxiv.org/abs/2509.02083v1
• Date: Tue, 02 Sep 2025 08:33:45 GMT
• Title: Performance analysis of common browser extensions for cryptojacking detection
• Abstract summary: We've examined most popular extensions - MinerBlock, AdGuard AdBlocker, Easy Redirect && Prevent Cryptojacking, CoinEater and Miners Shield.<n>An empirically confirmed dataset of 373 distinct cryptojacking-infected websites was used to test those extensions.<n>Easy Redirect and Miners Shield only blocked 6 and 5 websites respectively, while MinerBlock had the greatest detection rate at only 27%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper considers five extensions for Chromium-based browsers in order to determine how effective can browser-based defenses against cryptojacking available to regular users be. We've examined most popular extensions - MinerBlock, AdGuard AdBlocker, Easy Redirect && Prevent Cryptojacking, CoinEater and Miners Shield, which claim to be designed specifically to identify and stop illegal cryptocurrency mining. An empirically confirmed dataset of 373 distinct cryptojacking-infected websites which was assembled during multi-stage procedure, was used to test those extensions. The results showed that all plugins in question had significant performance limits. Easy Redirect and Miners Shield only blocked 6 and 5 websites respectively, while MinerBlock had the greatest detection rate at only 27% (101/373 sites blocked). Most concerningly, despite promises of cryptojacking prevention, AdGuard (which has over 13 million users) and CoinEater were unable to identify any of the compromised websites. These results demonstrate serious flaws in cryptojacking detection products targeted for regular users, since even the best-performing specimen failed to detect 73% of attacks. The obvious difference between advertised capabilities and real performance highlights the urgent need for either accessibility improvements for laboratory-grade detection technologies that show 90%+ efficiency in controlled environment or fundamental upgrades to current commonly used extensions.

Related papers

• Detecting Bot Detection: Prevalence, Techniques, and Implications for Web Measurement Research [0.5352699766206808]
  We conduct a measurement study of 10,000 websites across four browser configurations to quantify detection prevalence and employed techniques.<n>We develop a taxonomy of bot detection techniques and measure how often they appear in practice.<n>These findings demonstrate that bot detection creates systematic, provider-correlated sample loss that the web measurement community neither measures nor reports.
  arXiv  Detail & Related papers  (2026-06-12T14:59:11Z)
• CryptoGuard: Lightweight Hybrid Detection and Response to Host-based Cryptojackers in Linux Cloud Environments [9.40606834287371]
  CryptoGuard is a lightweight hybrid solution that combines detection and remediation strategies to counter cryptojackers.<n>It decomposes the classification task into a two-phase process, leveraging deep learning models to identify suspicious activity with high precision.<n>It achieves average F1-scores of 96.12% and 92.26% across the two phases, and outperforms state-of-the-art baselines in terms of true and false positive rates.
  arXiv  Detail & Related papers  (2025-10-21T06:15:48Z)
• From Blocking to Breaking: Evaluating the Impact of Adblockers on Web Usability [14.498659516878718]
  We aim to assess the extent of web breakages caused by adblocking on live sites using automated tools. The study also outlines the challenges and limitations encountered when measuring web breakages in real-time.
  arXiv  Detail & Related papers  (2024-10-30T23:25:07Z)
• MASKDROID: Robust Android Malware Detection with Masked Graph Representations [56.09270390096083]
  We propose MASKDROID, a powerful detector with a strong discriminative ability to identify malware. We introduce a masking mechanism into the Graph Neural Network based framework, forcing MASKDROID to recover the whole input graph. This strategy enables the model to understand the malicious semantics and learn more stable representations, enhancing its robustness against adversarial attacks.
  arXiv  Detail & Related papers  (2024-09-29T07:22:47Z)
• Next Generation of Phishing Attacks using AI powered Browsers [0.0]
  The model had an accuracy of 98.32%, precision of 98.62%, recall of 97.86%, and an F1-score of 98.24%. The zero-day phishing attack detection testing over a 15-day period revealed the model's capability to identify previously unseen threats. The model had successfully detected phishing URLs that evaded detection by Google safe browsing.
  arXiv  Detail & Related papers  (2024-06-18T12:24:36Z)
• Did I Vet You Before? Assessing the Chrome Web Store Vetting Process through Browser Extension Similarity [3.7980955101286322]
  We characterize the prevalence of malware and other infringing extensions in the Chrome Web Store (CWS), the largest distribution platform for this type of software. Our study reveals significant gaps in the CWS vetting process, as 86% of infringing extensions are extremely similar to previously vetted items. Our study also reveals that only 1% of malware extensions flagged by the CWS are detected as malicious by anti-malware engines.
  arXiv  Detail & Related papers  (2024-06-01T09:17:01Z)
• FV8: A Forced Execution JavaScript Engine for Detecting Evasive Techniques [53.288368877654705]
  FV8 is a modified V8 JavaScript engine designed to identify evasion techniques in JavaScript code. It selectively enforces code execution on APIs that conditionally inject dynamic code. It identifies 1,443 npm packages and 164 (82%) extensions containing at least one type of evasion.
  arXiv  Detail & Related papers  (2024-05-21T19:54:19Z)
• 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)
• Analyzing In-browser Cryptojacking [16.599890339599586]
  We analyze the static, dynamic, and economic aspects of in-browser cryptojacking. We apply machine learning techniques to distinguish cryptojacking scripts from benign and malicious JavaScript samples. We also build an analytical model to empirically evaluate the feasibility of cryptojacking as an alternative to online advertisement.
  arXiv  Detail & Related papers  (2023-04-26T02:46:42Z)
• Blockchain Large Language Models [65.7726590159576]
  This paper presents a dynamic, real-time approach to detecting anomalous blockchain transactions. The proposed tool, BlockGPT, generates tracing representations of blockchain activity and trains from scratch a large language model to act as a real-time Intrusion Detection System.
  arXiv  Detail & Related papers  (2023-04-25T11:56:18Z)
• DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified Robustness [58.23214712926585]
  We develop a certified defense, DRSM (De-Randomized Smoothed MalConv), by redesigning the de-randomized smoothing technique for the domain of malware detection. Specifically, we propose a window ablation scheme to provably limit the impact of adversarial bytes while maximally preserving local structures of the executables. We are the first to offer certified robustness in the realm of static detection of malware executables.
  arXiv  Detail & Related papers  (2023-03-20T17:25:22Z)
• Mate! Are You Really Aware? An Explainability-Guided Testing Framework for Robustness of Malware Detectors [49.34155921877441]
  We propose an explainability-guided and model-agnostic testing framework for robustness of malware detectors. We then use this framework to test several state-of-the-art malware detectors' abilities to detect manipulated malware. Our findings shed light on the limitations of current malware detectors, as well as how they can be improved.
  arXiv  Detail & Related papers  (2021-11-19T08:02:38Z)

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.