Aiming at the problems of low detection accuracy and poor generalization ability of Wreless Sensor Network （WSN） intrusion detection methods on imbalanced datasets with discrete high-dimensional features， an intrusion detection method for WSN based on Bidirectional Circulation Generative Adversarial Network was proposed， namely BiCirGAN. Firstly， Adversarially Learned Anomaly Detection （ALAD） was introduced to improve the understandability of the original features by reasonably representing the high-dimensional， discrete original features through the latent space. Secondly， the bidirectional circulation adversarial structure was adopted to ensure the consistency of bidirectional circulation in real space and latent space， thereby ensuring the stability of Generative Adversarial Network （GAN） training and improving performance of anomaly detection. At the same time， Wasserstein distance and spectral normalization optimization methods were introduced to improve the objective function of GAN to further solve the problems of mode collapse of GAN and lack of diversity of generators. Finally， because the statistical properties of intrusion attack data changed in an unpredictable way over time， a full connection layer network with Dropout operation was established to optimize the anomaly detection results. Experimental results on KDD99， UNSW-NB15 and WSN_DS datasets show that compared to Anomaly detection with GAN （AnoGAN）， Bidirectional GAN （BiGAN）， Multivariate Anomaly Detection with GAN （MAD-GAN） and ALAD methods， BiCirGAN has a 3.9% to 33.0% improvement in detection accuracy， and the average inference speed is 4.67 times faster than that of ALAD method.

Current neighborhood rough sets have been usually used to solve complete information system, not incomplete system. In order to solve this problem, an extended tolerance relation was proposed to deal with the incomplete mixed information system, and associative definitions were provided. The degree of complete tolerance and neighborhood threshold were used as the constraint conditions to find the extended tolerance neighborhood. The attribute importance of the system was got by the decision positive region within the neiborhood, and the attribute reduction algorithm based on the extended tolerance relation was proposed, which was given by the importance as the heuristic factor. Seven different types of data sets on UCI database was used for simulation, and the proposed method was compared with Extension Neighborhood relation (EN), Tolerance Neighborhood Entropy (TRE) and Neighborhood Rough set (NR) respectively. The experimental results show that, the proposed algorithm can ensure accuracy of classification, select less attributes by reduction. Finally, the influence of neighborhood threshold in extended tolerance relation on classification accuracy was discussed.

To improve the running efficiency of business workflow in cloud environment, a policy of replica allocation of cloudy services was proposed. Taking the advantage of social network analysis, the policy specified the central service nodes in a service network based on mining the social network properties such as connectivity and centralization for a service community. The host physical machine of the replica of the central service was specified according to the analysis of logical sequence between the central service and its pre-service, and the usage of other physical machines. The analysis and simulation show that the policy can improve the running efficiency of data intensive business workflow in a cloud environment by averaging the overload of physical machine and reducing the time wasted by long-distance service interaction.