Cryptic Bytes: WebAssembly Obfuscation for Evading Cryptojacking Detection

• URL: http://arxiv.org/abs/2403.15197v1
• Date: Fri, 22 Mar 2024 13:32:08 GMT
• Title: Cryptic Bytes: WebAssembly Obfuscation for Evading Cryptojacking Detection
• Authors: Håkon Harnes, Donn Morrison,
• Abstract summary: We present the most comprehensive evaluation of code obfuscation techniques for WebAssembly to date. We obfuscate a diverse set of applications, including utilities, games, and crypto miners, using state-of-the-art obfuscation tools like Tigress and wasm-mutate. Our dataset of over 20,000 obfuscated WebAssembly binaries and the emcc-obf tool publicly available to stimulate further research.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: WebAssembly has gained significant traction as a high-performance, secure, and portable compilation target for the Web and beyond. However, its growing adoption has also introduced new security challenges. One such threat is cryptojacking, where websites mine cryptocurrencies on visitors' devices without their knowledge or consent, often through the use of WebAssembly. While detection methods have been proposed, research on circumventing them remains limited. In this paper, we present the most comprehensive evaluation of code obfuscation techniques for WebAssembly to date, assessing their effectiveness, detectability, and overhead across multiple abstraction levels. We obfuscate a diverse set of applications, including utilities, games, and crypto miners, using state-of-the-art obfuscation tools like Tigress and wasm-mutate, as well as our novel tool, emcc-obf. Our findings suggest that obfuscation can effectively produce dissimilar WebAssembly binaries, with Tigress proving most effective, followed by emcc-obf and wasm-mutate. The impact on the resulting native code is also significant, although the V8 engine's TurboFan optimizer can reduce native code size by 30\% on average. Notably, we find that obfuscation can successfully evade state-of-the-art cryptojacking detectors. Although obfuscation can introduce substantial performance overheads, we demonstrate how obfuscation can be used for evading detection with minimal overhead in real-world scenarios by strategically applying transformations. These insights are valuable for researchers, providing a foundation for developing more robust detection methods. Additionally, we make our dataset of over 20,000 obfuscated WebAssembly binaries and the emcc-obf tool publicly available to stimulate further research.

Related papers

• Impact of Code Transformation on Detection of Smart Contract Vulnerabilities [0.0]
  This paper presents a method for improving the quantity and quality of smart contract vulnerability datasets. The approach centers around semantic-preserving code transformation, a technique that modifies the source code structure without altering its semantic meaning. The improved results show that many newly created vulnerabilities can bypass tools and the false reporting rate goes up to 100%.
  arXiv  Detail & Related papers  (2024-10-29T03:08:25Z)
• 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)
• JAMDEC: Unsupervised Authorship Obfuscation using Constrained Decoding over Small Language Models [53.83273575102087]
  We propose an unsupervised inference-time approach to authorship obfuscation. We introduce JAMDEC, a user-controlled, inference-time algorithm for authorship obfuscation. Our approach builds on small language models such as GPT2-XL in order to help avoid disclosing the original content to proprietary LLM's APIs.
  arXiv  Detail & Related papers  (2024-02-13T19:54:29Z)
• Evading Deep Learning-Based Malware Detectors via Obfuscation: A Deep Reinforcement Learning Approach [8.702462580001727]
  Adversarial Malware Generation (AMG) is the generation of adversarial malware variants to strengthen Deep Learning (DL)-based malware detectors. In this study, we show that an open-source encryption tool coupled with a Reinforcement Learning (RL) framework can successfully obfuscate malware. Our results show that the proposed method improves the evasion rate from 27%-49% compared to widely-used state-of-the-art reinforcement learning-based methods.
  arXiv  Detail & Related papers  (2024-02-04T20:23:15Z)
• SoK: Analysis techniques for WebAssembly [0.0]
  WebAssembly is a low-level bytecode language that allows languages like C, C++, and Rust to be executed in the browser at near-native performance. Vulnerabilities in memory-unsafe languages, like C and C++, can translate into vulnerabilities in WebAssembly binaries. WebAssembly has been used for malicious purposes like cryptojacking.
  arXiv  Detail & Related papers  (2024-01-11T14:28:13Z)
• Transformer-based Vulnerability Detection in Code at EditTime: Zero-shot, Few-shot, or Fine-tuning? [5.603751223376071]
  We present a practical system that leverages deep learning on a large-scale data set of vulnerable code patterns. We show that in comparison with state of the art vulnerability detection models our approach improves the state of the art by 10%.
  arXiv  Detail & Related papers  (2023-05-23T01:21:55Z)
• 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)
• Tweaking Metasploit to Evade Encrypted C2 Traffic Detection [5.156484100374058]
  Command and Control (C2) communication is a key component of any structured cyber-attack. Pentesting tools, such as Metasploit, generate constant traffic patterns that are easily distinguishable from regular web traffic. We show that a machine learning-based detector is able to detect the presence of such traffic with high accuracy, even when encrypted.
  arXiv  Detail & Related papers  (2022-09-02T10:56:15Z)
• Prototype-supervised Adversarial Network for Targeted Attack of Deep Hashing [65.32148145602865]
  deep hashing networks are vulnerable to adversarial examples. We propose a novel prototype-supervised adversarial network (ProS-GAN) To the best of our knowledge, this is the first generation-based method to attack deep hashing networks.
  arXiv  Detail & Related papers  (2021-05-17T00:31:37Z)
• Adversarial EXEmples: A Survey and Experimental Evaluation of Practical Attacks on Machine Learning for Windows Malware Detection [67.53296659361598]
  adversarial EXEmples can bypass machine learning-based detection by perturbing relatively few input bytes. We develop a unifying framework that does not only encompass and generalize previous attacks against machine-learning models, but also includes three novel attacks. These attacks, named Full DOS, Extend and Shift, inject the adversarial payload by respectively manipulating the DOS header, extending it, and shifting the content of the first section.
  arXiv  Detail & Related papers  (2020-08-17T07:16:57Z)

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.