Statically Detecting JavaScript Obfuscation and Minification Techniques in the Wild
Marvin Moog, Markus Demmel, Michael Backes, Aurore Fass
Abstract
JavaScript is both a popular client-side programming language and an attack vector. While malware developers transform their JavaScript code to hide its malicious intent and impede detection, well-intentioned developers also transform their code to, e.g., optimize website performance. In this paper, we conduct an in-depth study of code transformations in the wild. Specifically, we perform a static analysis of JavaScript files to build their Abstract Syntax Tree (AST), which we extend with control and data flows. Subsequently, we define two classifiers, benefitting from AST-based features, to detect transformed samples along with specific transformation techniques. Besides malicious samples, we find that transforming code is increasingly popular on Node.js libraries and client-side JavaScript, with, e.g., 90% of Alexa Top 10k websites containing a transformed script. This way, code transformations are no indicator of maliciousness. Finally, we showcase that benign code transformation techniques and their frequency both differ from the prevalent malicious ones.