Gender is an important aspect of a person's identity. In many applications, gender identification is useful for personalizing services and recommendations. On the other hand, many people today spend a lot of time on their mobile phones. Studies have shown that the way users interact with mobile phones is influenced by their gender. But the existing methods for identify the gender of mobile phone users are either not accurate enough or require sensors and specific user activities. In this paper, for the first time, the internet usage patterns are used to identify the gender of mobile phone users. To this end, the interaction data, and specially the internet usage patterns of a random sample of people are automatically recorded by an application installed on their mobile phones. Then, the gender identification is modeled using different machine learning classification methods. The evaluations showed that the internet features play an important role in recognizing the users gender. The linear support vector machine was the superior classifier with the accuracy of 85% and F-measure of 85%.