The continuous expansion in scale of multi-field entries and the growing increase in bit-width bring heavy storage pressure in hardware on the Internet. In order to solve this problem, a compression method based on Bit Extraction of Independent rule Subsets (BEIS) was proposed. Firstly, some fields were merged based on the logical relationships among multiple match fields, thus reducing the number of match fields and the width of flow tables. Secondly, with the division of independent rule subsets for the merged rule set, some differentiate bits in the divided subsets were extracted to achieve the matching and searching function, further reducing the used Ternary Content Addressable Memory (TCAM) space. Finally, the lookup hardware architecture of this method was put forward. Simulation results show that, with certain time complexity, the storage space of the proposed method can be reduced by 20% compared with Field Trimmer (FT) in OpenFlow flow table; in addition, for common packet classification rule sets such as access control list and firewall in practical application, the compression ratio of 20%-40% can be achieved.

To solve the problem existing in traffic classification that Peer-to-Peer (P2P) traffic is much more than that of non-P2P, a new traffic classification method for imbalanced network data was presented. By introducing and improving Synthetic Minority Over-sampling Technique (SMOTE) algorithm, a Mean SMOTE (M-SMOTE) algorithm was proposed to realize the balance of traffic data. On the basis of this, three kinds of machine learning classifiers:Random Forest (RF), Support Vector Machine (SVM), Back Propagation Neural Network (BPNN) were used to identify the various types of traffic. The theoretical analysis and simulation results show that, compared with the imbalanced state, the SMOTE algorithm improves the recognition accuracy of non-P2P traffic by 16.5 percentage points and raises the overall recognition rate of network traffic by 9.5 percentage points. Compared with SMOTE algorithm, the M-SMOTE algorithm further improves the recognition rate of non-P2P traffic and the overall recognition rate of network traffic by 3.2 percentage points and 2.6 percentage points respectively. The experimental results show that the way of imbalanced data classification can effectively solve the problem of low P2P traffic recognition rate caused by excessive P2P traffic, and the M-SMOTE algorithm has higher recognition accuracy rate than SMOTE.