June 28, 2022, 4:03 p.m. | Maria Zorkaltseva

Towards AI - Medium pub.towardsai.net

In this article, we will consider approaches to detect obfuscated JavaScript code snippets using machine learning.

Photo by Gabriel Heinzer on Unsplash

Introduction

Most websites use JavaScript (JS) code to make dynamic content; thus, JS code becomes a valuable attack vector against browsers, browser plug-ins, email clients, and other JS applications. Among common JS-based attacks are drive-by-download, cross-site scripting (XSS), cross-site request forgery (XSRF), malvertising/malicious advertising, and others. Most of the malicious JS codes are obfuscated in order to hide …

ai code cybersecurity javascript learning machine machine learning python

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