Over the years, the amount of data shared between users from different areas have grown considerably. Consequently, so did network attacks. Security monitoring strategies must classify information types on networks quickly and effectively. Intrusion Detection Systems have been proposed with Machine Learning techniques and High-Performance Computing to avoid security anomalies. Thus, FPGA devices are good candidates to improve performance and energy efficiency. In this brief, we propose a reconfigurable FPGA-based K-means/K-modes architecture to accelerate the data clustering for network intrusion detection. We evaluated our approach over NSL-KDD data set and the results showed that K-means and K-modes can achieve up to 15× and 994× more operations per Watt than parallel software versions.