The rapid increase in wired Internet speed and the constant growth in the number of attacks make network protection a challenge. Intrusion detection systems (IDSs) play a crucial role in discovering suspicious activities and also in preventing their harmful impact. Existing signature-based IDSs have significant overheads in terms of execution time and memory usage mainly due to the pattern matching operation. Therefore, there is a need to design an efficient system to reduce overhead. This research intends to accelerate the pattern matching operation through parallelizing a matching algorithm on a multi-core CPU. In this paper, we parallelize a bit-vector algorithm, Myers algorithm, on a multi-core CPU under the MapReduce framework. On average, we achieve four times speedup using our multi-core implementations when compared to the serial version. Additionally, we use two implementations of MapReduce to parallelize the Myers algorithm using Phoenix++ and MAPCG. Our MapReduce parallel implementations of the Myers algorithm are compared with an earlier message passing interface (MPI)-based parallel implementation of the algorithm. The results show 1.3 and 1.7 times improvement for Phoenix++ and MAPCG MapReduce implementations over MPI respectively.

中文翻译：

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MapReduce框架下使用Myers算法的基于签名的IDS模式匹配

有线互联网速度的快速提高和攻击数量的不断增长使网络保护成为一个挑战。入侵检测系统（IDS）在发现可疑活动并防止其有害影响方面起着至关重要的作用。现有的基于签名的IDS在执行时间和内存使用方面存在大量开销，这主要归因于模式匹配操作。因此，需要设计一种有效的系统以减少开销。本研究旨在通过并行化多核CPU上的匹配算法来加速模式匹配操作。在本文中，我们在MapReduce框架下的多核CPU上并行化了位向量算法Myers算法。平均而言，与串行版本相比，我们使用多核实现的速度提高了四倍。此外，我们使用MapReduce的两种实现方式使用Phoenix ++和MAPCG并行化Myers算法。我们将Myers算法的MapReduce并行实现与该算法基于早期消息传递接口（MPI）的并行实现进行了比较。结果表明，Phoenix ++和MAPCG MapReduce实现分别比MPI改进了1.3倍和1.7倍。