FARIS: Fast and Memory-Efficient URL Filter on CPU and GPGPU

Abstract

Uniform resource locator (URL) filtering is a fundamental technology for intrusion detection, HTTP proxies, content distribution networks, content-centric networks, and many other application areas. Some applications adopt URL filtering to protect user privacy from malicious or insecure websites. Some web browser extensions, such as AdBlock Plus, provide a URL-filtering mechanism for sites that intend to steal sensitive information. Unfortunately, these extensions are implemented inefficiently, resulting in a slow application that consumes much memory. Although it provides a domain-specific language (DSL) to represent URLs, it internally uses regular expressions and does not take advantage ofthe benefits ofthe DSL. In addition, the number of filter rules become large, which makes matters worse. In this paper, we propose the fast uniform resource identifier-specific filter, which is a domain-specific pseudo-machine for the DSL, to dramatically improve the performance of some browser extensions. Compared with a conventional implementation that internally adopts regular expressions, our proof-of-concept implementation is fast and small memory footprint.