1. A method for classifying a network traffic flow of a network, the method comprising: providing a first subspace, corresponding to a first classification, from a first training traffic flow; representing content independent parameters of the network traffic flow in a stochastic process model, wherein the content independent parameters comprise packet arrival time and packet size of each packet of a plurality of packets in the network traffic flow; extracting, using a computer, a first power spectral density (PSD) feature vector from the network traffic flow according to a spectral analysis of the stochastic process model; measuring, using the computer, a first similarity of the first PSD feature vector with the first subspace according to a first similarity metric; and identifying the first classification of the network traffic flow according to the first similarity wherein providing the first subspace comprises: representing content independent training parameters of the first training traffic flow in another stochastic process model, wherein the content independent training parameters of the first training traffic flow comprise packet arrival time and packet size of each training packet of a plurality of training packets in the first training network traffic flow; extracting using a computer, a plurality of PSD feature vectors from the first training traffic flow according to the spectral analysis of the other stochastic process model; composing the first subspace using the plurality of PSD feature vectors; decomposing the first subspace into one or more segments; and identifying one or more bases each representing a structure of a segment.