With the rapid development of technology, the scale of traffics in industrial control networks is increasing day by day. More malicious traffics brought terrible impacts on industrial areas. Modbus plays a momentous role in the communications of Industrial Control Systems (ICS), but it’s vulnerable to Denial of Service attacks(DoS). Traditional methods cannot perform well on fine-grained detection tasks which could contribute to locating targets of DoS and preventing the destruction. Considering the temporal locality and high dimension of malicious traffic, we proposed a Neural Network architecture named MODLSTM, which consists of three parts: input preprocessing, feature recoding, and traffic classification. By virtue of the design, MODLSTM can perform high-precision identification and fine-grained classification of DOS attacks in the Modbus/TCP-based system. To test our model’s performances, we conducted experiments on our traffic dataset collected from the industrial control network, and the models achieved excellent performances in comparison with previous work(accuracy increased by 0.74%). The results show that our proposed method has satisfactory abilities to detect DoS attacks related to Modbus, it could help to build a reliable firewall to address the DoS traffic in industrial environments.