© The Author(s). 2017. 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.