As one of the main Internet applications, search engine can retrieve and return effective information from Internet resources according to user needs. However, the obtained returned list often contains noisy information such as advertisements and invalid Web pages, which interfere the user's search and query. Aiming at the complex structural features and rich semantic information of Web pages, a Web page blacklist discrimination method based on attention mechanism and ensemble learning was proposed. And, by using this method, an Ensemble learning and Attention mechanism-based Convolutional Neural Network (EACNN) model was built to filter useless Web pages. First, according to different categories of HTML tag data on Web pages, multiple Convolutional Neural Network (CNN) base learners based on attention mechanism were established. Second, an ensemble learning method based on Web page structural features was used to perform different weight computation to the output results of different base learners to realize the construction of EACNN. Finally, the output result of EACNN was used as the analysis result of Web page content to realize the discrimination of Web page blacklist. The proposed method focuses on the semantic information of Web pages through attention mechanism, and introduces the structural features of Web pages through ensemble learning. Experimental results show that, compared with baseline models such as Support Vector Machine (SVM), K-Nearest Neighbor ( KNN), CNN, Long Short-Term Memory (LSTM) network, Gate Recurrent Unit (GRU) and Attention-based CNN (ACNN), EACNN has the highest accuracy (0.97), recall (0.95) and F ₁ score (0.96) on the geographic information field-oriented discrimination dataset constructed. It verifies the advantages of EACNN in the task of discriminating Web page blacklist.

Aiming at the unstructured characteristics of speech signal, a method of speaker authentication based on quantum tunneling effect was proposed. Based on quantum tunneling effect, the quantum properties of speech signal framing analyzed, and each speech signal frame was regarded as a quantum state, and the quantization of the algorithm was realized. And then the potential barrier was used to separate the energy characteristics. The barrier group was constructed to extract the energy spectrum characteristics of the signal and used it as the characteristic parameter. The speech signal modeling was finally carried out by the Gaussian Mixture Model (GMM) to complete the authentication of the speaker. The simulation results show that compared with the traditional method, the use of quantum tunneling theory to achieve speaker identification can reduce the complexity of algorithm effectively, improve the discrimination and provide a new direction for speaker authentication and quantum information theory.

It has been proved in many researches that there are some design flaws in IEEE 802.1X standard. In order to eliminate the Denial of Service (DoS) attack, replay attack, session hijack, Man-In-the-Middle (MIM) attack and other security threats, the protocol was analyzed in view of the state machines. It is pointed out that the origin of these problems is the inequality and incompleteness of state machines as well as the lack of integrity protection and source authenticity on messages. However, an improvement proposal called Dual-way Challenge Handshake and Logoff Authentication was proposed, and a formal analysis was done on it with an improved BAN logic. It is proved that the proposal can effectively resist the security threats mentioned above.