@Article{cmc.2020.07736,
AUTHOR = {Likun Liu, Jiantao Shi, Xiangzhan Yu, Hongli Zhang, Dongyang Zhan},
TITLE = {Skipping Undesired High-Frequency Content to Boost DPI  Engine},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {63},
YEAR = {2020},
NUMBER = {2},
PAGES = {649--661},
URL = {http://www.techscience.com/cmc/v63n2/38535},
ISSN = {1546-2226},
ABSTRACT = {Deep Packet Inspection (DPI) at the core of many monitoring appliances, such as 
NIDS, NIPS, plays a major role. DPI is beneficial to content providers and censorship to 
monitor network traffic. However, the surge of network traffic has put tremendous pressure on 
the performance of DPI. In fact, the sensitive content being monitored is only a minority of 
network traffic, that is to say, most is undesired. A close look at the network traffic, we found 
that it contains many undesired high frequency content (UHC) that are not monitored. As 
everyone knows, the key to improve DPI performance is to skip as many useless characters as 
possible. Nevertheless, researchers generally study the algorithm of skipping useless characters 
through sensitive content, ignoring the high-frequency non-sensitive content. To fill this gap, 
in this literature, we design a model, named Fast AC Model with Skipping (FAMS), to quickly 
skip UHC while scanning traffic. The model consists of a standard AC automaton, where the 
input traffic is scanned byte-by-byte, and an additional sub-model, which includes a mapping 
set and UHC matching model. The mapping set is a bridge between the state node of AC and 
UHC matching model, while the latter is to select a matching function from hash and fingerprint 
functions. Our experiments show promising results that we achieve a throughput gain of 1.3-
2.6 times the original throughput and 1.1-1.3 times Barr’s double path method.},
DOI = {10.32604/cmc.2020.07736}
}