In order to solve the problems that traditional machine learning based abnormal flow detection methods rely heavily on features, and the detection methods based on deep learning are inefficient and easy to overfit, an abnormal flow detection method based on Improved one-Dimentional Convolutional Neural Network (ICNN-1D) was proposed, namely AFM-ICNN-1D. Different from "convolution-pooling-full connection" structure of the traditional CNN, the ICNN-1D is mainly composed of 2 convolutional layers, 2 global pooling layers, 1 dropout layer and 1 fully connected output layer. The preprocessed data were put into ICNN-1D, and the result after two convolutional layers was used as the input of the global average pooling layer and the global maximum pooling layer, then the obtained output data were merged and sent to the fully connected layer to classify. The model was optimized according to the classification result and the real dataset, then it was used to the abnormal flow detection. The experimental results on the CIC-IDS-2017 dataset showed that the accuracy and recall rate of AFM-ICNN-1D reached 98%, which is better than that of the comparative k-Nearest Neighbor (kNN) and Random Forest (RF) methods. Moreover, compared with traditional CNN, the model parameters were reduced by about 97%, and the training time was shortened by about 40%. Experimental results show that AFM-ICNN-1D has high detection performance, which can reduce training time and avoid over fitting with better retaining the local characteristics of traffic data.