Assessing Adaptive Attacks Against Trained JavaScript Classifiers.

In this work, we evaluate the security of heuristicand machine learning-based classifiers for the detection of malicious JavaScript code. Due to the prevalence of web attacks directed though JavaScript injected into webpages, such defense mechanisms serve as a last-line of defense by classifying individual scripts as either benign or malicious. State-of-the-art classifiers work well at distinguishing currently-known malicious scripts from existing legitimate functionality, often by employing training sets of known benign or malicious samples. However, we observe that real-world attackers can be adaptive, and tailor their attacks to the benign content of the page and the defense mechanisms being used to defend the page. In this work, we consider a variety of techniques that an adaptive adversary may use to overcome JavaScript classifiers. We introduce a variety of new threat models that consider various types of adaptive adversaries, with varying knowledge of the classifier and dataset being used to detect malicious scripts. We show that while no heuristic defense mechanism is a silver bullet against an adaptive adversary, some techniques are far more effective than others. Thus, our work points to which techniques should be considered best practices in classifying malicious content, and a call to arms for more advanced classification.