In recent years, mobile traffic has gradually become a major part of network traffic. To attract customers, mobile network operators provide free-traffic, which is a preferential policy that is free of charge for specific application traffic. Since the emergence of free-traffic, fake free-traffic also appeared soon. Fake free-traffic is a malicious behavior, which helps attackers illegally use network resources and evade network resource charging. The appearance of fake free-traffic maliciously harms the interests of operators and disrupts the rules of network resource charging. Because of the uniqueness of free-traffic, it encapsulates a layer of the HTTP protocol in addition to the actual application communication protocol, existing studies on encrypted traffic analysis are not applicable to identify fake free-traffic. In this paper, we propose Burst Series Based Approach (BSBA), a novel method for identifying fake free-traffic. The key idea behind BSBA is to construct effective features by capturing the differences of burst series among fake free-traffic, free-traffic and non-free traffic, and combine the constructed features with machine learning algorithms to identify fake free-traffic. We collect a real-world traffic dataset and conduct evaluations to verify the effectiveness of the BSBA. Experiment results demonstrate that the BSBA achieves excellent performances (96.82% Accuracy, 96.46% Precision, 96.57% Recall and 96.51% F1-score) and is superior to the state-of-the-art methods.