WebAssembly Diversification for Malware Evasion

12/16/2022
by   Javier Cabrera-Arteaga, et al.
0

WebAssembly is a binary format that has become an essential component of the web nowadays. Providing a faster alternative to JavaScript in the browser, this new technology has been embraced from its early days to create cryptomalware. This has triggered a solid effort to propose defenses that can detect WebAssembly malware. Yet, no defensive work has assumed that attackers would use evasion techniques. In this paper, we study how to evade WebAssembly cryptomalware detectors. We propose a novel evasion technique based on a state-of-the-art WebAssembly binary diversifier. We use the worldwide authoritative VirusTotal as malware detector to evaluate our technique. Our results demonstrate that it is possible to automatically generate variants of WebAssembly cryptomalware, which evade the considered strong detector. Remarkably, the variants introduce limited performance overhead. Our experiments also provide novel insights about which WebAssembly code transformations are the best suited for malware evasion. This provides insights for the community to improve the state of the art of WebAssembly malware detection.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/25/2022

Multi-view Representation Learning from Malware to Defend Against Adversarial Variants

Deep learning-based adversarial malware detectors have yielded promising...
research
06/10/2019

Malware Detection with LSTM using Opcode Language

Nowadays, with the booming development of Internet and software industry...
research
02/01/2021

DRLDO: A novel DRL based De-ObfuscationSystem for Defense against Metamorphic Malware

In this paper, we propose a novel mechanism to normalize metamorphic and...
research
05/17/2022

A two-steps approach to improve the performance of Android malware detectors

The popularity of Android OS has made it an appealing target to malware ...
research
03/01/2018

The Shape of Alerts: Detecting Malware Using Distributed Detectors by Robustly Amplifying Transient Correlations

We introduce a new malware detector - Shape-GD - that aggregates per-mac...
research
06/23/2023

Full Transparency in DBI frameworks

Following the increasing trends of malicious applications or cyber threa...
research
09/15/2019

I-MAD: A Novel Interpretable Malware Detector Using Hierarchical Transformer

Malware imposes tremendous threats to computer users nowadays. Since sig...

Please sign up or login with your details

Forgot password? Click here to reset