Persistent flow is an important feature of hidden network attack. It does not generate a large amount of traffic and it occurs regularly in a long period, so that it brings a large challenge for traditional detection methods. Network attacks have invisibility, single monitors have heavy load and limited information. Aiming at the above problems, a method to detect network-wide persistent flows was proposed. Firstly, a sketch data structure was designed and was deployed on each monitor. Secondly, when the network flow arrived at a monitor, the summary information was extracted from network data stream and one bit in the sketch data structure was updated. Thirdly, at the end of measurement period, the summary information from other monitors was synthesized by the main monitor. Finally, the approximate estimation of flow persistence was presented. A bit vector was constructed for each flow by some simple computing, flow persistence was estimated by using probability statistical method, and the persistent flows were detected based on revised persistence estimation. The experiments were conducted on real network traffic, and their results show that compared with the algorithm of Tracing Long Duration flows (TLF), the proposed method increases the accuracy by 50% and reduces the false positive rate, false negative rate by 22%, 20% respectively. The results illustrate that the method of detecting network-wide persistent flows can effectively monitor network traffic in high-speed networks.