Open Access

ARTICLE

Skipping Undesired High-Frequency Content to Boost DPI Engine

Likun Liu¹, Jiantao Shi^{1, *}, Xiangzhan Yu¹, Hongli Zhang¹, Dongyang Zhan²

1 School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China.
2 Department of Computer Science and Engineering, The Ohio State University, Columbus, 43202, USA.

* Corresponding Author: Jiantao Shi. Email: .

Computers, Materials & Continua 2020, 63(2), 649-661. https://doi.org/10.32604/cmc.2020.07736

Received 23 June 2019; Accepted 16 July 2019; Issue published 01 May 2020

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.

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