With the rapid development of Internet of Things （IoT） and industrial Internet， the research of cyberspace security has been paid more and more attention by industry and academia. Because the source code cannot be obtained， binary code similarity search has become a key core technology for vulnerability mining and malware code analysis. Firstly， the basic concepts of binary code similarity search and the framework of binary code similarity search system were introduced. Secondly， the development status of binary code technology about syntax similarity search， semantic similarity search and pragmatic similarity search were discussed. Then， the existing solutions were summarized and compared from the perspectives of binary hash， instruction sequence， graph structure， basic block semantics， feature learning， debugging information recovery and advanced semantic recognition of functions. Finally， the future development direction and prospect of binary code similarity search were looked forward to.

Alzheimer’s Disease （AD） is a progressive neurodegenerative disease with hidden causes， and can result in structural changes of patients’ brain regions. For assisting the doctors to make correct judgment on the condition of AD patients， an improved Three-Dimensional Principal Component Analysis Network （3DPCANet） model was proposed to classify AD by combining the mean Amplitude of Low-Frequency Fluctuation （mALFF） image of the whole brain of the subject. Firstly， functional Magnetic Resonance Imaging （fMRI） data were preprocessed， and the mALFF image of the whole brain was calculated. Then， the improved 3DPCANet deep learning model was used for feature extraction. Finally， Support Vector Machine （SVM） was used to classify features of AD patients with different stages. Experimental results show that the proposed model is simple and robust， and has the classification accuracies on Subjective Memory Decline （SMD） vs. AD， SMD vs. Late Mild Cognitive Impairment （LMCI）， and LMCI vs. AD reached 92.42%， 91.80% and 89.33% respectively， which verifies the effectiveness and feasibility of the proposed method.

In IP network, tomography method can perform fault diagnosis by analyzing the end-to-end properties with low costs. However, most existing tomography based techniques have the following problems: 1) the end-to-end detected number is not sufficient to determine the state of each link; 2) as the scale of the network goes up, the diagnosis time may become unacceptable. To address these problems, a new congested link diagnosis algorithm based on Bayesian model was proposed in this paper. This method firstly modeled the problem as a Bayesian network, and then simplified the network by two steps and limited the number of multiple congested links. Therefore, the proposed method could greatly reduce the computational complexity and guarantee the diagnostic accuracy. Compared with the existing diagnosis algorithm which is called Clink, the proposed algorithm has higher diagnostic accuracy and shorter diagnosis time.