基于ML代理的GA优化构建高效正则表达式匹配器

Jonathan Hillblom, Johan Garcia, Anders Waldenborg
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引用次数: 0

摘要

流分类、入侵检测等重要的网络功能往往依赖于高通量正则表达式匹配。为了实现高性能,正则表达式可以表示为状态机,然后将状态机合并。然而,确定哪些单独的状态机应该理想地合并在一起是一个具有挑战性的优化问题。我们通过使用具有新的问题特定算子的遗传算法来解决这个问题。为了允许对新算子进行大规模评估,我们设计了两个基于ml的代理模型,用于昂贵的适应度评估函数。我们从一组生产规模正则表达式得到的结果表明,使用最合适的操作可以提供比朴素基线更大的收益,但也不存在通用的最佳操作组合。我们提供了一些关于哪些操作符在不同目标下表现最好的见解,并展示了特定于TCP和udp的正则表达式之间的差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Building Efficient Regular Expression Matchers Through GA Optimization With ML Surrogates
Important network functions such as traffic classification and intrusion detection often depend on high-throughput regular expression matching. To achieve high performance, regular expressions can be represented as state machines, which are then merged. However, determining which individual state machines should ideally be merged together is a challenging optimization problem. We address this problem by using genetic algorithms with novel problem-specific operators. To allow large scale evaluation of the new operators, we devise two ML-based surrogate models for the expensive fitness evaluation function. Our results from a set of production scale regular expressions show that using the most appropriate operations provides large gains over a naive baseline, but also that no universal best combination of operators exist. We provide some insights into which operators perform best for different objectives, and show the variation between TCP- and UDP-specific regular expressions.
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