Hyperflex: A SIMD-based DFA Model for Deep Packet Inspection

TL;DR

Hyperflex is a SIMD-optimized DFA model that significantly improves deep packet inspection throughput by enabling high-speed regex matching on commodity CPUs, outperforming traditional DFA models.

Contribution

It introduces a novel SIMD-based DFA model with region detection and hybrid transition algorithms, enhancing regex matching performance for DPI applications.

Findings

Achieves up to 2.27x throughput improvement over existing DFA models.

Reaches a throughput of 8.89 Gbit/s on real network traffic.

Successfully deployed in Hyperscan, boosting its performance.

Abstract

Deep Packet Inspection (DPI) has been extensively employed for network security. It examines traffic payloads by searching for regular expressions (regex) with the Deterministic Finite Automaton (DFA) model. However, as the network bandwidth and ruleset size are increasing rapidly, the conventional DFA model has emerged as a significant performance bottleneck of DPI. Leveraging the Single-Instruction-Multiple-Data (SIMD) instruction to perform state transitions can substantially boost the efficiency of the DFA model. In this paper, we propose Hyperflex, a novel SIMD-based DFA model designed for high-performance regex matching. Hyperflex incorporates a region detection algorithm to identify regions suitable for acceleration by SIMD instructions across the whole DFA graph. Also, we design a hybrid state transition algorithm that enables state transition in both SIMD-accelerated and normal regions, and ensures seamless state transition across the two types of regions. We have implemented Hyperflex on the commodity CPU and evaluated it with real network traffic and DPI regexes. Our evaluation results indicate that Hyperflex reaches a throughput of 8.89Gbit/s, representing an improvement of up to 2.27 times over Mcclellan, the default DFA model of the prominent multi-pattern regex matching engine Hyperscan. As a result, Hyperflex has been successfully deployed in Hyperscan, significantly enhancing its performance.