In the Device-to-Device (D2D) cache network, the cache space in the mobile terminal is relatively small with many multimedia contents. In order to realize the efficient use of cache space in mobile terminals, a D2D cache deployment algorithm based on user preference and replica threshold was proposed. Firstly, based on the user preference, a cache revenue function to determine the cache value of caching each file was designed. Then, with the goal of maximizing the cache hit ratio of system, the cache replica threshold was designed based on convex programming theory to deploy replica number of the files in the system. Finally, combining the cache revenue function with the replica threshold, a heuristic algorithm was proposed to implement file cache deployment. Compared with the existing cache deployment algorithm, the proposed algorithm can significantly improve the cache hit rate and the offload gain with the reduction of service delay.

In view of the fact that users' preferences and social interaction data are very sparse, and the fact that users may prefer products recommended by friends than recommended by foes, a regularized matrix decomposition recommendation algorithm integrating with social network and interest preference similarity was proposed. First of all, for the problem of sparse data of social relations. Global and local topological characteristics of the network were used to extract trust and distrust matrices between users respectively. Secondly, a method for calculating interest preference similarity between users was defined. Finally, in the process of matrix decomposition, the trust matrix, the distrust matrix, and the interest correlation were synthetically taken into consideration to make recommendations for the users. Experiments show that this method is superior to other regularization recommendation methods. Compared with the basic matrix decomposition model (SocialMF), SoRec, TrustMF, CTRPMF and RecSSN algorithm, the proposed algorithm reduces 1.1% to 9.5% and 2% to 10.1% respectively in the root mean square error (RMSE) and the mean absolute error (MAE), improved recommendations effectively.

In data mining, mining maximum frequent itemsets instead of mining frequent itemsets can greatly improve the operating efficiency of system. The running time consumption of existing maximum frequent itemset mining algorithms is still very large. In order to solve the problem, a new DiffNodeset Maxmal Frequent Itemset Mining (DNMFIM) algorithm was proposed. Firstly, a new data structure DiffNodeset was adopted to realize the fast calculation of intersection and support degree. Secondly, the connection method with linear time complexity was adopted to reduce the complexity of connecting two DiffNodesets and avoid multiple invalid calculations. Then, the set-enumeration tree was adopted as the search space, and a variety of optimal pruning strategies were used to reduce the search space. Finally, the superset detection technology used in the MAximal Frequent Itemset Algorithm (MAFIA) algorithm was adopted to improve the accuracy of algorithm effectively. The experimental results show that, DNMFIM algorithm outperforms MAFIA and N-list based MAFIA (NB-MAFIA) in terms of time efficiency. The proposed algorithm has a good performance when mining the maximal frequent itemsets in different types of datasets.

Concerning the problem that untrusted sample can be easily introduced in traditional methods, an adaptive model was proposed in this paper. Based on the description of the structural feature of Request-URL, a whole sample set was divided into smaller subsets. The discreteness of a subset was calculated by its properties, which would determine whether the subset is normal. On basis of these, the detection model was created by the improved algorithm with the normal subsets, and dynamic update of model was achieved by Hidden Markov Model (HMM) merging. The experimental results show that the adaptive model built by the proposed method can effectively identify Web-based attacks and reduce false alert ratio.

In order to reduce the outage probability and enhance the stability of cognitive system, an improved algorithm of spectrum allocation based on classical graph coloring model was proposed. A difference factor of spectrum's idle time and user's request time was introduced. For every cognitive user, the algorithm allocated spectrums according to two factors: the spectrum efficiency and the time difference factor. Cognitive user with greater product value of the two factors was prior. The simulation results show that the outage probability of improved algorithm is far below that of the previous algorithm.