Aiming at the problem of poor recommendation effect caused by the data sparsity of the recommendation system, a hybrid recommendation algorithm based on rating filling and trust information was proposed namely RTWSO (Real-value user item restricted Boltzmann machine Trust Weighted Slope One). Firstly, the improved restricted Boltzmann machine model was used to fill the rating matrix, so as to alleviate the sparseness problem of the rating matrix. Secondly, the trust and trusted relationships were extracted from the trust relationship, and the matrix decomposition based implicit trust relationship similarity was also used to solve the problem of trust relationship sparsity. The modification including trust information was performed to the original algorithm, improving the recommendation accuracy. Finally, the Weighted Slope One (WSO) algorithm was used to integrate the matrix filling and trust similarity information as well as predict the rating data. The performance of the proposed hybrid recommendation algorithm was verified on Epinions and Ciao datasets. It can be seen that the proposed hybrid recommendation algorithm has the recommendation accuracy improved by more than 3% compared with the composition algorithm, and recommendation accuracy increased by more than 1.2% compared with the existing social recommendation algorithm SocialIT (Social recommendation algorithm based on Implict similarity in Trust). Experimental results show that the proposed hybrid recommendation method based on rating filling and trust information, improves the recommended accuracy to a certain extent.

To solve low detection rate problem of intrusion for a small number of samples in mass unbalanced datasets, an anomaly detection based on Synthetic Minority Oversampling Technique (SMOTE) and Deep Belief Network (DBN), called SMOTE-DBN method, was proposed. Firstly, SMOTE technology was used to increase the number of samples in minority categories. Secondly, on the preprocessed balanced data set, the dimensionality of the preprocessed high-dimensional data was reduced by unsupervised Restricted Boltzmann Machine (RBM). Thirdly, the model parameters were finely tuned by Back Propagation (BP) algorithm to obtain the lower low-dimensional representation of the preprocessed data. Finally, softmax classifier was used to classify the optimal low-dimensional data. The simulation experiment results on KDD1999 show that, compared with DBN method and Support Vector Machine (SVM) method, the detection rate of SMOTE-DBN method is increased by 3.31 and 7.34 percentage points respectively, and the false alarm rate is decreased by 1.11 and 2.67 percentage points respectively.

Distributed Denial of Service (DDoS) attack is a serious threat to network security. In order to solve this problem, an effective method of tracing DDoS attack was proposed based on Autonomous System (AS) and Dynamic Probabilistic Packet-Marking (DPPM). In the proposed method, a new scheme of packet marking was designed with setting up two markers as the domain marks and routing tags for inter-domain tracing and in-domain tracing. Domain marks and routing tags were set at the same time using dynamic packet marking methods. Finally, through the path reconstruction on in-domain and inter-domain, the attack node was traced back rapidly. The experimental results show that the proposed algorithm is efficient and feasible, which provides an important basis for the DDoS attack prevention.