Recent research on Internet traffic classification has yield a number of data mining techniques for distinguishing types of traffic, but no systematic analysis on "Why" some algorithms achieve high accuracies. In pursuit of empirically grounded answers to the "Why" question, which is critical in understanding and establishing a scientific ground for traffic classification research, this paper reveals the three sources of the discriminative power in classifying the Internet application traffic: (i) ports, (ii) the sizes of the first one-two (for UDP flows) or four-five (for TCP flows) packets, and (iii) discretization of those features. We find that C4.5 performs the best under any circumstances, as well as the reason why; because the algorithm discretizes input features during classification operations. We also find that the entropy-based Minimum Description Length discretization on ports and packet size features substantially improve the classification accuracy of every machine learning algorithm tested (by as much as 59.8%!) and make all of them achieve >93% accuracy on average without any algorithm-specific tuning processes. Our results indicate that dealing with the ports and packet size features as discrete nominal intervals, not as continuous numbers, is the essential basis for accurate traffic classification (i.e., the features should be discretized first), regardless of classification algorithms to use.

In this paper, we first propose an analytical model of WLANs (wireless LANs) with an arbitrary backoff distribution and AIFS (arbitration inter-frame space). From the analysis, we show that the achievable bandwidth is determined by the mean of backoff distribution regardless of the shape of the backoff distribution. We compare the effectiveness of four parameters on channel access differentiation, namely, the mean of backoff distribution, CW/sub min/ (initial contention window), the number of backoff stages, and AIFS. Numerical results show that the mean of backoff distribution provides weighted fair channel access most accurately. Second, based on the proposed analytic frame work, we develop three schemes for the uplink/downlink bandwidth differentiation in order to achieve weighted fairness between uplink and downlink transmissions. Note that IEEE 802.11 is known to have unfairness between uplink and downlink accesses. Each scheme is characterized according to the corresponding channel access rule. The simulation results show that the proposed schemes achieve high system throughput while accurately differentiating bandwidth allocation.