An Efficient Caching Mechanism for Network-Based URL Filtering by Multi-Level Counting Bloom Filters

Network-based URL filtering (NUF) is one of the most widely used tools for detecting and stopping malicious and unwanted web traffic, like preventing children from sex. However, currently the conventional techniques still suffer from high bandwidth consumption due to millions of URL analysis requests to the network servers per day. In this paper, a model of NUF using a novel multi-level counting bloom filter (MLCBF) is proposed to address this issue. In the gateways of NUF, MLCBF is used to cache the analysis results from the network server to accelerate web traffic, alleviate the server load, and reduce bandwidth consumption of the entire NUF service. Analysis and trace-based experiments are employed to explore the properties of MLCBF and evaluate its performance in NUF. The results show that the proposed scheme typically eliminates at least 90% of memory requirements as compared to a general hashing table solution.