© 2015 IEEE. This paper proposes a novel approach to separating background (BG) and foreground (FG) traffic based on periodicity analysis. As BG traffic is commonly periodically generated by applications, this trait is leveraged to effectively detect BG traffic. Concretely, the Period Candidate Array (PCA) approach is proposed to extract only necessary information from long and sparse traffic flows, hence quickly detects the flows' periodicity with low computational cost. The PCA works directly with ''on-site'' traffic without depending on historical data as in machine learning methods. As a result, the proposed approach can be immediately applied to the real world traffic management systems. In addition, the PCA properly works with latency-included traffic affected by network delays. Experimental results reveal the effectiveness and efficiency of the PCA compared to the conventional methods in terms of computational cost, memory usage, and independence to historical data.