It is difficult for existing rumor detection methods to overcome the disadvantage of data collection and detection delay. To resolve this problem, a rumor detection method based on burst topic detection inspired by the momentum model and domain expert discovery was proposed. The dynamics theory in physics was introduced to model the topic features spreading among the Weibo platform, and dynamic physical quantities of the topic features were used to describe the burst characteristics and tendency of topic development. Then, emergent topics were extracted after feature clustering. Next, according to the domain relativity between the topic and the expert, domain experts for each emergent topic were selected within experts pool, which is responsible for identifying the credibility of the emergent topic. The experimental results show that the proposed method gets 13 percentage points improvement on accuracy comparing with the Weibo rumor identification method based merely on supervised machine learning, and the detection time is reduced to 20 hours compared with dominating manual methods, which means that the proposed method is applicable for real rumor detection situation.

Aiming at the noise data problem in collaborative filtering recommendation, a user entropy model was put forward. The user entropy model combined the concept of entropy in the information theory and used the information entropy to measure the content of user information, which filtered the noise data by calculating the entropy of users and getting rid of the users with low entropy. Meanwhile, combining the user entropy model with the item timeliness model, the item timeliness model got the timeliness of item by using the contextual information of the rating data, which alleviated the data sparsity problem in collaborative filtering algorithm. The experimental results show that the proposed algorithm can effectively filter out noise data and improve the recommendation accuracy, its recommendation precision is increased by about 1.1% compared with the basic algorithm.

With the rapid development of Internet bandwidth, the parallel processing technique can greatly improve the performance of network intrusion detection system. Network Intrusion Detection System (NIDS) in parallel environment requires complete connection while balancing the traffic load. That is, packets belonging to one session should go to the same processing node. Based on the stability and balance characteristics of B+ tree, this paper proposed a feedback load balancing algorithm based on B+ tree fast tuning. B+ tree has characteristics of high search efficiency and stability. This algorithm tuned the B+ tree structure and remapped flow table when unbalanced. The simulation results show that this algorithm is able to balance the connection density of B+ tree, achieves a really satisfactory balance of the sensors' load and reduces the packet loss rate.