Pubblicazione scientifica

Massive Fishing Website URL Parallel Filtering Method

FILONE TECNOLOGICO	TEMA
economia circolare e sostenibilità

RIASSUNTO

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.

DATA

Data di pubblicazione: 12/12/2017

AUTORI

DONGLIANG XU
JINGCHANG PAN
XIAOJIANG DU
BAILING WANG
MENG LIU
QINMA KANG

ENTE DI AFFERENZA

SCHOOL OF MECH ELEC & INFO ENG SHANDONG UNIV WEIHAI WEIHAI CHINA
DEPT COMPUTER & INFO SCIENCES TEMPLE UNIV PHILADELPHIA PA USA
SCHOOL OF COMPUTER SCIENCE & TECH HARBIN INST TECH WEIHAI CHINA

RIVISTA

IEEE Access (Volume: 6, Page(s): 2378-2388)

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