Effective Analysis, Characterization, and Detection of Malicious Web Pages

The steady evolution of the Web has paved the way for mis-creants to take advantage of vulnerabilities to embed mali-cious content into web pages. Up on a visit, malicious web pages steal sensitive data, redirect victims to other mali-cious targets, or cease control of victim’s system to mount future attacks. Approaches to detect malicious web pages have been reactively effective at special classes of attacks like drive-by-downloads. However, the prevalence and complex-ity of attacks by malicious web pages is still worrisome. The main challenges in this problem domain are (1) fine-grained capturing and characterization of attack payloads (2) evolu-tion of web page artifacts and (3) flexibility and scalability of detection techniques with a fast-changing threat landscape. To this end, we proposed a holistic approach that leverages static analysis, dynamic analysis, machine learning, and evo-lutionary searching and optimization to effectively analyze and detect malicious web pages. We do so by: introduc-ing novel features to capture fine-grained snapshot of mali-cious web pages, holistic characterization of malicious web pages, and application of evolutionary techniques to fine-tune learning-based detection models pertinent to evolution of attack payloads. In this paper, we present key intuition and details of our approach, results obtained so far, and future work