In this article, we will consider approaches to detect obfuscated JavaScript code snippets using machine learning. 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 what they are doing and to avoid being detected by signature-based security systems. In other words, the obfuscation technique is a sequence of confusing code transformations to compromise its understandability, but at the same time to save its functionality.

This is the last part of a 3 articles serie. In the first part, we introduced neural networks and TensorFlow framework basics. In the second part, we explained how to convert existing models from Python to TensorFlow.js Finally, we present today, through an example, how to use an online TensorFlow.js model and deploy it rapidly using our WarpJS JavaScript Serverless Function-as-a-Service (FaaS). Many public models can be retrieved from web databases.