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.

Single biometric identification technology can be easily affected by various external factors, thus the recognition rate and stability are poor. A palmprint and palmvein image fusion recognition algorithm based on super-wavelet domain, namely NSCT-NBP, was proposed. Firstly, palmprint and palmvein images were decomposed by using Non-Subsampled Contourlet Transform (NSCT), then the obtained low-frequency and high-frequency sub-images were respectively merged by using the regional energy and image self-similarity principle. Secondly, the texture features were extracted from the fused images by using Neighbor based Binary Pattern (NBP), thus the eigenvector was got. Finally, the similarity of the fused images was calculated by Hamming distance of the feature vectors, to get Equal Error Rate (EER). The experiments were conducted on PolyU and a self-built database, the experimental results show that the lowest EER of NSCT-NBP algorithm were 0.72% and 0.96%, the identification time were only 0.0530 s and 0.0871 s. Compared with the current best palmprint-palmvein fusion method based on wavelet transform and Gabor filter, the EER of the two databases were reduced by 4% and 36.8%, respectively. The NSCT-NBP algorithm can effectively fuse the texture features of the palmprint-palmvein images and has good recognition performance. The fusion of palmprint-palmvein features can enhance the security of the recognition system.

Concerning the flow rule tampering attacks and other single point vulnerability threats towards Software Defined Network (SDN) controller, traditional security solutions such as backup and fault-tolerant mechanisms which are based on passive defense defects, cannot fundamentally solve the control layer security issues. Combined with the current moving target defense and cyberspace mimic defense, a dynamic security scheduling mechanism based on heterogeneous redundant structure was proposed. A controller scheduling model was established in which the dynamic scheduling strategy was designed based on security principle combined with attack exception and heterogeneity. By considering the system load, the scheduling problem was transformed into a dynamic two-objective optimization problem by LA-SSA (Load-Aware Security Scheduling Algorithm) to achieve an optimal scheduling scheme. Simulation results show that compared with static structure, the dynamic scheduling mechanism has obvious advantages in cumulative number of exceptions and output safety rate, and the dynamic and diversity in the security scheduling mechanism can significantly improve the system's ability to resist attacks.The load variance of LA-SSA is more stable than that of safety priority scheduling, and the security imbalance is avoided, and the effectiveness of the security scheduling mechanism is verified.

Concerning the low accuracy, simple algorithm and multiple parameters but difficulty in modification of hotbox detection of Infrared Train Hotbox Detecting System (THDS), a new hotbox detection model based on fuzzy neural networks was proposed. The model selected three variables as inputs, such as temperature difference, train temperature difference and vehicle temperature difference, and four hotbox grades as outputs. One hundred and twenty-five fuzzy rules and learning algorithm were used to train the fuzzy neural networks, which can be as expert system to detect hot axis. The practical simulation results show that the hotbox detection model using fuzzy neural networks can reduce the number of detecting parameters, and the discrete concordance rate reaches 95%.