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Massive Fishing Website URL Parallel Filtering Method
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DOI：10.1109/ACCESS.2017.2782847.png)
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摘要
En 中文
A randomized fingerprint model is proposed, which can effectively reduce the false positive rate by generating a unique fingerprint for each URL. The model is also used to improve the Wu and Manber (WM) algorithm, which is a multi-string matching algorithm; as a result, a randomized fingerprint WM (RFP-WM) algorithm is proposed. Furthermore, a Graphics Processing Unit (GPU)-based parallel randomized fingerprint algorithm (GRFP-WM) is implemented. Experimental results indicate that, for a massive pattern set containing more than a million URLs, the efficiency of the RFP-WM algorithm is 20% higher than that of the WM algorithm. The WM algorithm's efficiency is approximately 7% higher than that of the Aho and Corasick (AC) algorithm, which is also a multi-string matching algorithm. The efficiency and speedup of the GRFP-WM algorithm are higher than those of the GPU-based WM and the GPU-based AC algorithms. These results indicate that the randomized fingerprint model can effectively reduce the collision rate and improve the efficiency of the algorithm.
Keyword:
URL filtering
randomized fingerprint model
GRFP-WM
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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引用论文
A Lightweight Multicast Authentication Mechanism for Small Scale IoT Applications.png)
IEEE SENSORS JOURNAL
IEEE SENSORS JOURNALIF4.5
PREAI
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